1

A proposal submitted to NOAA for

NOAA Climate Program Office FY 2015 – NOAA-OAR-CPO-2015-2004099

MAPP Competition: Process-oriented evaluation of climate and Earth system models and

derived projections (Area A, Type 2): Competition ID: 2488569

CFDA Number: 11.431: Climate and Atmospheric Research

Diurnal Metrics for Evaluating GFDL and Other Climate Models

October 16, 2014

Dr. Aiguo Dai, Lead Principal Investigator

Associate Professor

University at Albany – SUNY Dept. of Atmospheric & Environmental Sciences

1400 Washington Avenue, Albany, NY 12222

Tel. 518-442-4474; email: adai@albany.edu

Dr. Jean-Christophe (Chris) Golaz, Co-I

Physical Scientist

NOAA Geophysical Fluid Dynamics

Laboratory (GFDL)

201 Forrestal Road, Princeton, NJ 08540

Tel: 609-452-6523; email:

chris.golaz@noaa.gov

Dr. Junhong Wang, Co-PI

Research Associate Professor

University at Albany – SUNY Dept. of Atmospheric & Environmental Sciences

1400 Washington Avenue, Albany, NY 12222

Tel. 518-442-3478; email:

jwang20@albany.edu

Dr. Ming Zhao, Co-I

Project Specialist III

NOAA Geophysical Fluid Dynamics

Laboratory (GFDL)

201 Forrestal Road, Princeton, NJ 08540

Tel. 609-452-6500; email:

ming.zhao@noaa.gov

Institutional Representatives:

Jessie L. Beauharnois

Senior Administrative Staff Associate

The Research Foundation for The State

University of New York, University at Albany

Office for Sponsored Programs, MSC 312,

1400 Washington Avenue, Albany, NY 12222

Tel. 518-437-8663 Fax: 518-437-8758

Email: jbeauharnois@albany.edu

Wendy Marshall

Budget Analyst

NOAA Geophysical Fluid Dynamics

Laboratory

201 Forrestal Road

Princeton, New Jersey 08542

Tel. 609-452-6587

Email: Wendy.Marshall@noaa.gov

DUNS Number: 152652822

Duration of the Project: 1 August 2015 – 31 July 2018

Funding requested:

Year 1 Year 2 Year 3 Total 3 Years

SUNY: Dr. Aiguo Dai $143,198 $143,192 $143,198 $429,588

GFDL: Dr. Jean-Christophe Golaz $6,776 $6,776 $6,776 $20,328

TOTAL: $149,974 $149,968 $149,974 $449,916

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MAPP Competition: Process-oriented evaluation of climate and Earth system models and derived

projections (Area A, Type 2)

1. Abstract

The diurnal cycle is a fundamental feature of Earth's climate. Because of its short time scales and

close coupling to surface and atmospheric processes, the simulation of the diurnal cycle provides an ideal

test bed for evaluating many aspects of model physics. Despite recent improvements in model resolution

and parameterizations, the diurnal amplitude and phase in surface temperature, cloudiness, convection,

precipitation and other fields still differ considerably from observations in many climate models. These

diurnal biases reflect deficiencies in various physical processes simulated by the models. While there

exist many observational datasets with sub-daily resolution, most of them cannot be readily used to

evaluate models, and current model evaluation packages often contain very limited data for evaluating the

diurnal cycle. Based on our previous work on studying the diurnal cycle and its simulation in models,

here we propose to a) develop a new set of diurnal metrics and link them to specific underlying

processes for evaluating model physics, and b) apply the diurnal metrics to diagnose and identify

deficiencies in the GFDL and other CMIP5 models.

Specifically, we propose to 1) compile a new dataset with high temporal-resolution (hourly to 6-

hourly) from surface and satellite observations, field experiments, research sites, and atmospheric

reanalyses for studying the diurnal cycle and evaluating models; 2) apply the new dataset to quantify the

diurnal cycle and study its underlying processes in various fields over the globe, including surface daily

maximum (Tmax) and minimum (Tmin) temperatures, precipitation frequency, intensity and amount,

cloud cover, humidity and others; 3) design a new set of effective diurnal metrics and link them to

specific physical processes based on analyses of observational data; and 4) apply these diurnal metrics

and associated linkages to physical processes to diagnose deficiencies in GFDL and other CMIP5 models

by analyzing sub-daily output from these models.

The new diurnal data set and diurnal metrics developed in this project will greatly enhance current

model evaluation packages. Our second task will improve our understanding of the diurnal cycle and its

underlying physical processes. This understanding is necessary for developing constructive diurnal

metrics for evaluating physical processes in models, while tasks 3 and 4 will directly help improve

models, especially the GFDL model.

A unique feature of this proposal is that it utilizes the expertise of the PI and others on this proposal

in studying the diurnal cycle to identify specific physical processes underlying each of the major diurnal

variations (e.g., in Tmax and Tmin or the low-level jet over the central U.S.), so that a modeler can use

this information to examine specific areas in his/her model when a diurnal bias is found. Another strength

is that it includes two leading modelers from GFDL who have a strong desire to improve the simulation

of the diurnal cycle in GFDL's new models. This collaboration will lead to real model improvements.

Relevance: This proposal is for MAPP Competition - Process-oriented evaluation of climate and Earth system

models and derived projections (Area A, Type 2), which emphasizes projects to "develop and apply

process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and

observational bases". The diurnal cycle is a well-studied, fundamental feature of Earth's climate. The

focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to

underlying physical processes make our metrics truly process-oriented. We will also apply the new

diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this

proposal is directly responsive to the MAPP competition. Improving climate models and our

understanding of the diurnal cycle is also an important step to achieve NOAA's long-term climate goal to

"improved scientific understanding of the changing climate system and its impacts".

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2. Results from Prior Research The PI (A. Dai) has previously analyzed various surface and satellite data to study the diurnal cycle

in surface air temperature, pressure, humidity, winds, and precipitation, as well as the diurnal cycle in

climate models (see refs. cited in section 3.1). However, Dr. Dai has not had any specific projects in the

last 3 years funded by NOAA or other agencies that are relevant to this proposal, besides the one

mentioned below on homogenization of radiosonde data. During the last three years, Dr. Dai has

continued his diurnal research through collaborations with Curt Covey on evaluation of the pressure tides

in CMIP3 and CMIP5 models (Covey et al. 2011; 2014). He also updated his diurnal analysis of CMIP3-

model simulated precipitation (Dai 2006a) to include CMIP5 models, and provided an updated figure (cf.

Fig. 1) to IPCC Fifth Assessment Report (AR5, its Fig. 9.30). The PI's expertise on the diurnal cycle and

model diagnostics is critical for the proposed work, especially for designing the diurnal metrics and

linking them to specific underlying physical processes.

Co-PI J. Wang has been and continues to be involved in the following projects on creating high-

quality, high resolution data to study the diurnal cycle in water vapor and other fields along with other

time scales, and applying them to validate reanalysis products. Since 2011, Dr. Wang has been supported

by NASA ROSES program to update and improve her global, 2-hourly ground-based GPS precipitable

water (PW) dataset (Wang et al. 2007). The dataset has been used to study the PW diurnal variability,

validate radiosonde and satellite data and reanalysis products, and document the diurnal asymmetry in

PW trends and its correlation with temperature (Wang and Zhang 2008; Wang and Zhang 2009; Wang et

al. 2013; Wang et al. 2014). During 2010-2013, Dr. Wang and Dr. Dai led a project supported by NOAA

Climate Program Office on homogenizing global radiosonde humidity data and characterizing upper air

humidity variability. This project produced several journal publications (e.g., Dai et al 2011; Zhao et al.

2012; Wang et al. 2013a; Wang et al. 2014) and a homogenized radiosonde data set that has been used by

several groups already. From 1999 to August 2014, Dr. Wang worked on quality-control, bias-correction

and analysis of sounding data from various field projects (e.g., Wang et al. 2002, 2003; Ciesielski et al.

2014). The sounding data have been used to validate satellite and reanalysis products, and to create

gridded, 3-hourly or 6-hourly upper air datasets for projects with special designed sounding arrays (e.g.,

Wang et al. 2010; Wang et al. 2013b; Johnson et al. 2014).

Dr. Golaz has extensive experience with the development and evaluation of global climate models.

He contributed to the development of the CMIP5 generation GFDL AM3 model and is now co-leading

the team tasked with the development of the next generation atmospheric component AM4. Dr. Golaz

authored several publications related to the treatment of clouds in AM3 and the impact of the aerosol

indirect effects (Golaz et al. 2011, Golaz et al. 2013, Suzuki et al. 2013). PBL properties of AM3 were

also analyzed in the context of an intercomparison with other global models (Zhang et al. 2011, Seidel et

al. 2012). As part of a CPT project, Dr. Golaz also helped implement a new unified cloud and turbulence

parameterization in AM3 (CLUBB, Guo et al. 2014). This new parameterization unifies the treatment of

PBL mixing, shallow convection and cloud macro-physics scheme.

Dr. Zhao is currently a co-lead of atmospheric model working group (AWG) and coupled model

working group (CWG) for development of GFDL’s next generation climate model (AM4/CM4).

Previously, Zhao is a core developer of GFDL global high-resolution atmospheric model (HiRAM, Zhao

et al. 2009) and the atmospheric model version 3 (AM3, Donner et al. 2011). Zhao’s most recent work has

been focusing on developing a new double-plume convection scheme (DPC) for AM4/CM4. A new

version of HIRAM with the DPC scheme has been tested in both multi-decadal climate simulations forced

by the observed SSTs or radiative gases concentrations and various short-range (a few days to a season)

forecast experiments with initial values set to observed conditions. We have found many improvements in

climate simulations including both mean climate and tropical variability such as MJO statistics. The

causes of the model’s improvements are currently under exploration. Dr. Zhao is the lead PI on grant

recently funded by NOAA CPO (started in Sept 2014) to perform "Process Level Investigation of the Role

of Convection and Cloud Parameterization in Tropical Pacific Bias in GFDL Next Generation Global

Climate Models". He is also Co-PI on a NOAA CPO/MAPP grant led y X. Jiang of UCLA to study

MJO's influence on tropical cyclones (started in Sept 2012).

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3. Statement of Work 3.1 Introduction and the Problem Forced by the solar diurnal cycle, Earth's weather and climate show large sub-daily variations at

24-hr and shorter time scales (referred to as diurnal variations or the diurnal cycle hereafter) in air

temperature (Dai et al. 1999a), pressure (Dai and Wang 1999), winds (Dai and Deser 1999; Dai and

Trenberth 2004), cloudiness (Dai and Trenberth 2004), convection and precipitation (Dai 2001; Dai et al.

2007), water vapor (Dai et al. 2002) and other fields (Lin et al. 2000; Seidel et al. 2012). Because of its

short time scales and close coupling to near-surface and atmospheric processes, simulations of the diurnal

cycle in weather and climate models provide an excellent test bed for evaluating various model physics,

such as surface sensible heat (SH), latent heat (LH) and radiative fluxes, the planetary boundary layer

(PBL), cloud formation, moist convection, and precipitation process.

Many studies have examined model-simulated diurnal variations in surface temperature, pressure,

precipitation, the PBL height, and other fields (Randall et al. 1991; Holtslag and Boville 1993; Dai et al.

1999b; Lin et al. 2000; Dai & Trenberth 2004; Dai 2006a; Ploshay & Lau, 2010; Wang et al. 2011; Seidel

et al. 2012; Covey et al. 2011, 2014; Lindvall et al. 2013; Lindvall & Svensson, 2014). These studies

show that while most climate models are able to reproduce the observed diurnal and semidiurnal

variations in surface pressure fields (i.e., the pressure tides), many of them still have difficulties in

simulating the observed diurnal cycle in surface air temperature, moist convection, cloud cover,

precipitation (Fig. 1), PBL height, and other related surface fields, although improved resolution (Sato et

al. 2009; Ploshay and Lau 2010; Wang et al. 2011) and parameterizations (Khairoutdinov et al. 2005;

Stratton and Stirling 2012; Hourdin et al. 2013) help models simulate the diurnal cycle. In particular,

many climate models tend to rain too frequently at reduced intensity (i.e., drizzling with a weak diurnal

cycle) (Dai 2006a; Stephens et al. 2010), leading to their limited abilities in simulating heavy

precipitation and other extreme events. This is a major deficiency that limits a model's ability to simulate

future changes in extreme events under increased greenhouse gases (GHGs). Another issue is that many

coupled models lack a diurnal cycle in sea surface temperatures (SSTs) due to the coarse vertical

resolution in upper oceans and daily coupling between the atmosphere and oceans. This leads to a weak

diurnal cycle in air temperature and other related fields over the oceans (Dai and Trenberth, 2004) and

causes other problems (Danabasoglu et al., 2006; Bernie et al., 2008).

GFDL climate models (Delworth et al. 2006; Zhao et al. 2009; Donner et al. 2011; Guo et al. 2014)

are among the most respected and widely used models in climate research and projection. However, like

all other models used in the CMIP5 project (Taylor et al. 2012), the GFDL models still suffer from

deficiencies and would benefit from improvements in various aspects. In particular, the simulations of the

diurnal cycle by GFDL models still contain large biases, including a too small diurnal temperature range

(DTR) over the U.S. and other land areas, a near-noon peak (instead of a late-afternoon peak as in

observations) in convective and total precipitation over land areas (Fig.2).

The diurnal biases in the GFDL and other models reflect deficiencies in various model-simulated

physical processes, including the diurnal evolution of the near-surface heating, vertical turbulence mixing

of heat, water and momentum, the PBL, shallow and deep convective clouds, moist convection, etc. By

analyzing the model processes underlying the simulated diurnal variations, it is possible to diagnose and

identify the problems in model physics and parameterizations that lead to the diurnal biases. However,

this type of analysis requires a deep understanding of the underlying processes behind each diurnal cycle,

and the use of high-resolution data, for which the CMIP5 archive of model data is insufficient.

Furthermore, an in-depth understanding of the model physics is also necessary for such a diagnostic

analysis. Thus, a close collaboration with the modelers is essential. However, it is almost impossible to

have close interactions with many of the CMIP5 modeling groups at the same time. Partly because of

these reasons, most previous analyses of model-simulated diurnal cycle have focused on the comparison

of the mean diurnal variations between observations and models, but often fallen short of linking the

diurnal biases to potential problems in specific model physics or parameterizations. Because of this, these

studies are usually not directly beneficial to modelers for diagnosing and improving their models.

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Fig. 1. Composite diurnal cycle of precipitation for land and ocean latitude zones derived from 3-hourly data from

CMIP5 models. For most of the models, data from 1980-2005 from "historical runs" were averaged to derived the

composite cycle. The best models include MIRCO-ESM, CMCC-CM, and inmcm4. Different versions of the

models from the same center showed similar diurnal cycle for precipitation. Many models still tend to have a peak

precipitation soon after noon, several hours too early compared to surface (OBS) and TRMM satellite observations,

although the best performing models appear to be able to capture the land and ocean diurnal phase and amplitude

quite well. This is an update to Fig. 17 of Dai (2006a) and used by IPCC AR5 as its Fig. 9.30.

FIG.2: (a) Long-term (1981-2000) mean June-August (JJA) differences (oC) between model-simulated (GFDL

prototype AM4 model) and observed (from PRISM) surface diurnal temperature range (DTR). (b) JJA composite

diurnal cycle (departures from the daily mean in mm/day) averaged over global (50oS-50

oN) land for prototype

AM4 total (blue), convective (pink), and large-scale (brown) precipitation, compared with TRMM observed total

precipitation (green)

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Here we propose to develop a new set of diurnal metrics and establish the linkages between

these metrics and their controlling physical processes as seen in observations and reanalyses, so that

modelers can use these metrics to evaluate and diagnose specific underlying processes simulated in

their models. Without the linkage to specific physical processes, a diurnal bias may not be very

constructive for improving model physics as many modelers may not have the necessary expertise to link

such a bias to its underlying processes. We recognize that the diurnal cycle in one particular field (e.g.,

deep convection) is often coupled to several factors or processes, and it may be impossible to link a

diurnal bias (e.g., in convective precipitation) to one or two specific factors or processes. Nevertheless,

the physical processes underlying diurnal variations in surface pressure, daily maximum (Tmax) and

minimum (Tmin) temperature, DTR, SH, LH, and others are understood well enough so that a likely

linkage between a diurnal bias in each of these variables and specific physical processes can be

established. Such a linkage is critical for modelers to use the diurnal metrics to diagnose model

deficiencies.

We have formed a team of experts from SUNY Albany and GFDL to carry out the proposed work.

The lead PI (A. Dai) has done and published considerable amount of research on the diurnal cycle and its

depiction in NCAR and other climate models. He has an in-depth understanding of the characteristics and

the underlying physical processes for the diurnal cycle in many climate fields. Other members include Dr.

Junhong Wang, who is an expert on water vapor, atmospheric sounding, and field campaign datasets, and

Drs. Chris Golaz and Ming Zhao, who are experts in the study and modeling of the PBL, clouds and

convection. Chris and Ming jointly lead the GFDL Model Development Team Atmospheric Working

Group (AWG) for the development of GFDL's next atmospheric model, and they have a strong desire to

improve the diurnal simulations in GFDL's new models through this collaborative research. Thus, this

proposal combines the strength of the PI and co-PI from SUNY Albany in diurnal studies, datasets

and diagnostic analysis with the expertise in model development from GFDL's leading modelers.

This unique combination of expertise has the potential to make real model improvements through

the diurnal analysis work proposed below.

3.2 Scientific Objectives

The main objective of this proposal is to develop a new set of constructive diurnal metrics that

can guide modelers to identify problems in simulating certain physical processes in their models. A

secondary goal is to improve our understanding of the physical processes underlying major diurnal

variations. Such an improved understanding is necessary for designing these diurnal metrics and for

establishing their linkages to specific physical processes.

Although the diurnal cycle is evident in many climate fields, designing useful diurnal metrics that

can be compared with available observations and help reveal problems in underlying model physics

requires in-depth understanding of the physical processes behind the diurnal variations in the real world.

It also requires a good knowledge of the available observations with high temporal-resolution from in-situ

measurements and remote sensing. For example, the DTR in surface air temperature has been measured at

thousands of weather stations around the world, and it is closely coupled with cloud cover, surface

evaporation, surface albedo, and other near-surface processes (Dai et al. 1999a). Thus, biases in model-

simulated DTR can be compared with observations and these biases can be used to diagnose problems in

simulating these related fields. Another example is that warm-seasonal low-level clouds and moist

convection over land and coastal regions exhibit a large diurnal cycle, typically with shallow cumulus

clouds developing before noon, deep cumulus clouds appear in early afternoon, and thunderstorms occur

in late afternoon over the continents, as shown in surface and satellite observations (Dai et al. 2007),

although exceptions to this general pattern do exist over some regions such as the central Great Plains in

the U.S. (Dai et al. 1999b; Liang et al. 2004). This diurnal evolution of clouds and convection can be used

to diagnose problems in model parameterizations for the PBL, clouds, shallow and deep convection, as

well as surface energy fluxes.

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Thus, the design of the diurnal metrics and interpretation of model biases in these metrics involve

considerable knowledge about the physical processes behind the diurnal variations measured by these

metrics. A simple comparison of a diurnal metric (e.g., DTR) between observations and models without

proper understanding of the physics behind it will not be very useful to many modelers. A unique feature

of this proposal is that we will use our expertise from studying the diurnal cycle, the PBL, clouds

and convection to design appropriate diurnal metrics and link them to specific physical processes in

models, thereby helping modelers find specific areas in their models that may require

improvements given the biases revealed by the diurnal metrics. Thus, our diurnal metrics and the

linkages to specific physical processes will be helpful to not only our collaborators at GFDL, but also

other modeling groups, including those participated in CMIP5.

3.3 Proposed Work and Methodology

Specifically, we propose to 1) gather and compile sub-daily (hourly to 6-hourly) data from

observations and other sources (Table 1), 2) analyze these data to improve our understanding of the

physical processes behind the diurnal variations in various fields, 3) develop a new set of diurnal metrics

and establish the link to their underlying physical processes, and 4) apply the new diurnal metrics to

evaluate and diagnose model physics in the GFDL and other CMIP5 models. These individual tasks are

described below.

3.3.1 Compilation of sub-daily data from observations and reanalyses

We will first collect, quality-control, compile and synthesize available high temporal-resolution

data from observations and reanalysis products (Table 1) to produce a high-quality, comprehensive

dataset for diurnal analyses in this project and by others, including climate modeling groups.

There are numerous hourly, 3-hourly and 6-hourly data from weather and radiosonde stations (Dai

2001; Dai et al. 2011), special research projects (such as the ARM sites, Xie et al. 2010) around the globe,

special sounding arrays from field campaigns such as the IHOP (Weckwerth et al. 2004) over the Great

Plains, the TOGA-COARE (Webster and Lukas 1992) in the Western Pacific and the DYNAMO

(Yoneyama et al. 2013) over the Indian Ocean, as well as satellite observations (e.g., TRMM 3-hourly

products, Huffman et al. 2007), and reanalysis products (e.g., 3-hourly data from NARR, CFSR and

MERRA). Table 1 summarizes the major datasets that can be used in our analysis.

The sounding networks from various field projects (Fig. 3) show extensive spatial coverage. Until

August 2014, Co-PI J. Wang had been the lead scientist at NCAR in charge of the data quality control of

these sounding data from various field campaigns. We have extensive experiences in using the surface

and satellite data to study the diurnal cycle (e.g., Dai and Wang 1999; Dai and Deser 1999; Dai 2001; Dai

2006b; Dai et al. 2007; Wang and Zhang 2008; Wang et al. 2014) and in analyzing high-resolution model

output (e.g., Dai and Trenberth 2004; Dai 2006b). We have also used high-resolution data from field

experiments to study the diurnal cycle (e.g., Dai et al. 1999a) in surface temperature and the warm-season

precipitation over the central U.S. For example, Fig. 4 shows that related to the differential heating

between the Rockies and the Great Plains there exists a diurnal circulation over these regions that

suppresses daytime convection and favors nighttime convection over the Great Plains. Climate models

may need finer resolution (<100km) to simulate this regional diurnal circulation and thus the nocturnal

precipitation maximum in the central U.S.

Many of the individual datasets listed in Table 1 are not designed for comparisons with climate

models and they cannot be readily used in model evaluations (except for the gridded ones). As a result,

many model evaluation packages include only very limited data with sub-daily resolution. For example,

59 out of the 64 datasets included in the Observations for Climate Model Intercomparisons (Obs4MIPs)

project (https://www.earthsystemcog.org/projects/obs4mips/) contain monthly mean data, and only one

has sub-daily temporal resolution (3-hourly TRMM precipitation). We will examine and analyze the

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various data sets listed in Table 1 and compile a comprehensive diurnal data set that can be readily used

for model evaluations. In particular, we will use the compiled data set to compute our diurnal metrics and

to study the physical processes underlying these metrics.

FIG. 3: Distribution of field projects with high-resolution hourly sounding observations that can be used to evaluate

climate models. (From Johnson et al. 2012)

FIG. 4: Longitudinal variations of zonal and vertical wind profiles (arrows, from NARR) averaged from

N to 42N and over days without precipitation during the IHOP period from 13 May to 25 June 2002 at

(a) 09 UTC and (b) 21 UTC. The colors show vertical pressure velocity (in Pa/s, positive downward,

from NARR) in (a) and (b). (c) Diurnal cycle of vertical wind velocity profiles over the U.S. central Great

Plains averaged over days without precipitation during the IHOP period from NARR (in Pa/s).

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Table 1. Observational and reanalysis data sets to be used in this project. Dataset Name

(Reference)

Spatial &Temporal

Resol. & Coverage

Data Sources and Merging Method Online Documentation

Surface met data

3-hrly surface

synoptic data (Dai

2006b)

4°(lat)5°(lon),

globe; 3-hrly,

1975-2004

Surface synoptic obs from >15,000

stations, and ships; temperature,

humidity, pressure, clouds, precipitation

occurrence, and winds

Land:http://rda.ucar.edu/datase

ts/ds464.0/

Ocean: http://icoads.noaa.gov/

GHCN-daily

(Menne et al. 2012)

>90,000 stations,

globe land; daily,

1880-present

Land-based surface station data;

including Tmax, Tmin, precipitation,

snow and others

http://www.ncdc.noaa.gov/oa/c

limate/ghcn-daily/

Precipitation data

CMORPH1

(Joyce et al. 2004)

0.25o grid, 60°S -

60°N, 180°W -

180°E; 30 min.,

12/2002-present

Satellite microwave estimates and the

TRMM (TMI7) satellites are propagated by

motion vectors derived from geostationary

satellite infrared data.

http://www.cpc.ncep.noaa.gov/

products/janowiak/cmorph_des

cription.html

PERSIANN2

(Hsu et al. 1997)

0.25o grid, 60°S -

60°N, 180°W -

180°E; 30 min.,

3/2000-present

A neural network, trained by precipitation

from TRMM TMI (2A12) and other

satellites was used to estimate 30 min.

precipitation from infrared images from

global geo-satellites.

http://chrs.web.uci.edu/persian

n/data.html

TRMM 3B42

(Huffman et al.

2007)

0.25o grid, 50°S -

50°N, 180°W -

180°E; 3-hourly,

1/1998-present

Microwave precipitation estimates were

used to adjust IR estimates from

geostationary IR observations. The rainfall

estimates were scaled to match the monthly

rain gauge analysis used in TRMM 3B-43.

http://daac.gsfc.nasa.gov/preci

pitation/TRMM_README/T

RMM_3B42_readme.shtml

CPC Hourly US

Precipitation

(Higgins et al. 1996)

2.5 o lon 2.0o lat,

20oN-60oN, 140oW-

60oW; hourly,

7/1948-10/2002

Hourly reports from ~2800 rain gauges

were used to derive the gridded data.

http://www.cpc.ncep.noaa.gov/res

earch_papers/ncep_cpc_atlas/1/toc

.html

http://www.esrl.noaa.gov/psd/data/

gridded/data.cpc_hour.html

Global precipitation

frequency (Dai

2001)

2o grid, global; 3-

hourly for each

season, 1976-1997

Weather reports from ships and >15,000

stations were used to compile the

occurrence frequency for various types

of precipitation

http://www.cgd.ucar.edu/cas/a

dai/data-dai.html

GPCP v2.2

(Huffman et al.

2009)

2.5o grid, globe,

monthly, 1979-

present

IR estimates were calibrated by

microwave estimates and then adjusted

by rain-gauge data

http://precip.gsfc.nasa.gov/ http://www.esrl.noaa.gov/psd/data/

gridded/data.gpcp.html

Radiosonde data

6-hrly radiosonde

dataset (Seidel et al.

2005)

53 stations, globe;

3- or 6-hourly, 4-

mon to 4 years

Radiosonde data from global radiosonde

archive, TOGA_COARE and ARM

projects

Seidel et al. (2005)

IGRA2 6-hrly

radiosonde dataset

13 or more stations

(Fig.1), globe; 6-

hourly, various

NCDC IGRA Version 2 (more data,

longer records, including ships and Ice

islands)

http://www1.ncdc.noaa.gov/pu

b/data/igra/v2beta/

ARM

SONDEWRPR

ARM SGP sites; 3-6

hrly, 1994-present.

ARM Radiosonde data from SGP sites http://www.arm.gov/data/datas

treams/sondewrpr

High resolution

sounding data

(Johnson et al.2012)

Various sites,

globe; 3-6 hrly,

1958-present

High resolution radiosonde data from

various field projects (see Fig. 3)

http://www.eol.ucar.edu/projec

ts/legacy/

GPS data

A global, 2-hrly

GPS PW dataset

(Wang et al. 2007)

>400 stations,

globe; 2-hrly,

1995-present

Precipitable water (PW) derived from

ground-based GPS measurements

http://rda.ucar.edu/datasets/ds7

21.1/

Suominet data

(Ware et al. 2000)

~900 stations, globe;

30-min, 1995-present PW derived from ground-based GPS

measurements; concentrated in USA

http://www.suominet.ucar.edu/

data.html

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Table 1 (continued) GFZ TIGA reprocessed data (Tong Ning, pers.

comm.)

782 stations, globe;

30min, 1994-2012

Consistently processed GPS PW data http://www.gfz-

potsdam.de/en/research/organizational-units/departments/department-

1/gpsgalileo-earth-

observation/projects/tiga-reprocessing/

Cloud data

ISCCP D1 data

(Rossow and

Schiffer 1999)

280km equal-area-

grid, globe; 3hrly

1983-2009

Cloud amount and other properties

derived from satellite observations

https://eosweb.larc.nasa.gov/pr

oject/isccp/isccp_d1_table

ISCCP WS data

(Rossow et al. 2005) 2.5 2.5, globe;

3hrly 1983-2004

Weather state data derived from ISCCP

D1 data

http://isccp.giss.nasa.gov/clima

nal5.html

Field Project Data Objectives & data

ARM ARMBE data

set (Xie et al. 2010)

ARM sites from

Alaska to the

Tropics, resol.: min.

Clouds, radiation and other data http://www.arm.gov/data/vaps/

armbe

TOGA_COARE

(Webster and Lukas

1992)

1o1o, tropical west

Pacific; 6-hrly,

11/1992-2/1993

Studying convection, air-sea and

multiple-scale interactions; CSU

gridded datasets and others

http://tornado.atmos.colostate.edu/toga

data/gridded.html

http://www.eol.ucar.edu/projects/toga_coare/data.html

IHOP (Weckwerth

et al. 2004)

Five stations, US

SGP; 3-hrly, 5/26-

6/14/2012

Warm-season precipitation diurnal cycle

in SGP; sounding data, derived

CAPE/CIN, NARR and other data

http://data.eol.ucar.edu/master

_list/?project=IHOP_2002

NAME (Higgins et

al. 2006) 1

o1

o, N.A.

Monsoon region;

6-hrly, 7/1-

8/15/2004

N.A. Monsoon, its variability and

impact on N.A. warm season

precipitation; CSU gridded datasets and

others

http://tornado.atmos.colostate.edu/

name/products/gridded/index.html

http://data.eol.ucar.edu/master_list

/?project=NAME

DYNAMO

(Yoneyama et al.

2013)

1o1

o, Indian

Ocean; 3-hrly, 10-

12/2011

Understanding the processes key to

MJO initiation; CSU gridded datasets

and others

http://johnson.atmos.colostate.edu/dyn

amo/products/gridded/index.html

http://data.eol.ucar.edu/master_list

/?project=DYNAMO

FLUXNET

(Lindvall et al.

2013)

35 FLUXNET sites,

globe; 30/60 min.,

1992-2009

Turbulent fluxes, surface met. http://data.eol.ucar.edu/codiac/

dss/id=76.205

GCSS-DIME3 Various, globe;

various

Focus on cloud-climate interactions and

GCM evaluation; both satellite and in-situ

campaign data.

http://gcss-dime.giss.nasa.gov/

Reanalysis data

NARR, CFSR,

MERRA, ERA-

Interim

N.A. (NARR), global,

~0.5 t0 1.0o, 1-, 3- &

6-hrly, 1979-present

Surface and upper air fields, constrained

by satellite and radiosonde observations

and model physics.

http://reanalyses.org/atmospher

e/comparison-table

1 CMORPH = Climate Prediction Center (CPC) morphing method

2 PERSIANN = Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks

3 GCSS-DIME: GEWEX Cloud System Study Data Integration for Model Evaluation

3.3.2 Quantifying the diurnal cycle and identifying its underlying physical processes

Building on previous studies of the diurnal cycle done by ourselves and others (some of them cited

in section 3.1), we will further quantify the long-term mean diurnal amplitude and phase in various fields

to provide a comprehensive view of the mean diurnal cycle and its spatial and seasonal variations, and to

improve our understanding of the physical processes underlying the major diurnal variations such those in

surface temperatures, energy fluxes, cloudiness, convection and precipitation.

The quantification of the diurnal amplitude and phase will be straightforward, either using the

actual daily minimum and maximum of the composite mean diurnal curve (cf. Fig. 2b; e.g., for DTR,

precipitation, etc.) or by fitting the composite diurnal curve with the 24-hr and 12-hr harmonics (e.g., for

pressure tides, temperature, etc., see Dai and Wang 1999). Table 2 lists our choices for specific variables.

11

We prefer the method of deriving the composite diurnal curve first and then estimate the diurnal

(and semidiurnal) amplitude and the phase from this curve (as done in all of our previous diurnal studies)

over the method of applying a Fourier transfer directly to the original data series (e.g., Covey et al. 2011,

2014). The latter method requires that the data series be continuous without missing data, which is often

not the case for observations, and it needs more computing time than the first method. Furthermore, the

composite averaging over a large number of days during many years for each local hour removes synoptic

and other variations, leaving only the diurnal variations in the composite curve, which itself depicts the

diurnal cycle nicely (cf. Fig. 2b). The composite method also allows one to stratify the data by seasons.

The diurnal amplitude (A=the maximum minus the daily mean) and local solar time when the maximum

occurs (tmax) derived from high temporal-resolution composite diurnal curves will be used as the diurnal

metrics for many of the diurnal variables listed in Table 2. For cases where only relatively low-resolution

(e.g., 3-hourly or even coarser) data are available, we will fit the data curve with diurnal and semidiurnal

harmonics first and estimate A and tmax from the fitted curve.

A more challenging task will be to establish the physical linkage between a diurnal cycle (e.g., in

temperature) to near-surface and atmospheric processes that control it, similar to what we did for the DTR

in Dai et al. (1999a). Based on our current understanding, in Table 2 we list the likely physical processes

underlying the diurnal cycle in each of the major climate variables. The task here will be to use the newly

compiled diurnal dataset to quantify the linkage between a diurnal cycle and the associated physical

processes (some of them are listed in Table 2). Following Dai et al. (1999a), we will employ composite

analysis (e.g., clear vs. cloudy days, wet-surface vs. dry-surface days, windy vs. calm days, humid vs.

low-humidity days, etc.) to compare the diurnal cycle between different conditions, and other method

(e.g., partial correlations) to identify the major controlling factors or processes responsible for the diurnal

cycle in each of the variables listed in Table 2. We will also quantify the influence (e.g., using correlation

or variance explained) from each of these factors or processes, and derive their proper distributions or

characteristics for a correct representation of the diurnal cycle in a given field. For example, cloud amount

is found to be a major controlling factor for daytime maximum temperature (Tmax; Dai et al. 19999a);

thus, correct spatial and seasonal distributions of mean cloud amount in a model are necessary for it to

realistically simulate the Tmax and thus DTR for the right reason. Sometimes, models may still simulate

the correct Tmax even if cloud amount is incorrect because of cancellation of errors in different fields.

However, one would prefer a model to be correct for the right reason (this also applies to precipitation

frequency, intensity and amount, see Dai 2006a). Thus, it is essential to realistically simulate the cloud

amount first in order to correctly simulate Tmax and DTR in a model.

The linkage information, in additional to the diurnal metric itself, is critical for modelers to

evaluate the underlying factors or processes in their models. Thus, we will include it in our standard data

products from this project, using tables similar to Table 2 but with more specific information (e.g., with

rankings for the controlling factors). Required long-term climatology of these factors (e.g., surface albedo,

cloud cover, and daytime evapotranspiration for Tmax case, etc.) derived from observations and

reanalyses will also be included in the data products.

Our diurnal analysis will focus on three areas: 1) thermal heating near the surface and in the PBL, 2)

tropospheric conditions, and 3) clouds, moist convection, and precipitation. Table 2 summarizes the main

variables and likely underlying physical processes or factors for each of these areas based on our current

understanding. Table 2 provides a general guidance to our diurnal analysis, but new variables and

processes will likely be added as the work progresses.

3.3.3 Development of a new set of diurnal metrics linked to specific underlying processes

Based on the above analysis of the diurnal cycle and its underlying processes, we will develop a

new set of diurnal metrics that are linked to specific processes for evaluating and diagnosing model

physics. Table 2 lists some of the candidate variables and associated metrics. They include diurnal

variations for measuring surface daytime heating (e.g., Tmax, DTR) and nighttime cooling (e.g., Tmin),

12

tropospheric conditions (e.g., vertical velocity), and diurnal evolution of low clouds, convection and

precipitation. For most of the variables listed in Table 2, there exist high temporal-resolution data from

observations and/or reanalyses (Table 1), so that a comparison between models and observations or

reanalyses is possible. Some of the variables, such as convective available potential energy (CAPE),

convective inhibition (CIN) and low-level convergence, will need to be derived from other observational

fields such as atmospheric temperature and humidity profiles and winds from sounding observations or

reanalyses.

Besides the availability of observational data, another criterion in selecting the diurnal metrics will

be how strong a metric is coupled to certain specific processes or factors. For example, Dai et al. (1999a)

found that Tmax is tightly coupled to cloud amount through clouds' albedo effect on solar radiation, thus

the metric TmaxTmean, i.e., the deviation of the daily maximum temperature from the daily mean, can be

an effective measure of cloudiness, although surface evaporative cooling, surface reflection of sunlight

and surface SH flux can also influence Tmax. Below we use two examples to illustrate how our diurnal

metrics may help modelers diagnose problems in their models.

Suppose a modeler finds a mean bias in TmaxTmean compared with observed values over a region,

e.g., over the central U.S. in the GFDL AM4 model (Fig. 5a), then the first thing (s)he should check is the

model-simulated cloud amount. If the long-term mean cloud amount is too low (high) compared with

observations, then Tmax-Tmean will likely be too large (small), although compensating errors from the

other processes in the model may complicate this relationship seen in observations. Assuming that the

modeler does find biases in the simulated cloud amount, then (s)he will have to examine and test the

cloud scheme used in the model to find out what may cause the cloud biases. Although this single diurnal

metric will not help the modeler locate the exact problems in the cloud scheme, it is still very constructive

by pointing the modeler in the right direction. Furthermore, other diurnal metrics, such as those related to

the diurnal evolution of clouds and convection (Table 2), can also help the modeler find the problems in

the simulation of clouds. If the modeler finds the cloud amount reasonable, then (s)he will need to check

the other surface fields, and use other diurnal metrics and other diagnostics to find the underlying model

deficiencies.

The diurnal cycle over the U.S. Great Plains (Dai et al. 1999b; Liang et al. 2004) presents another

case for examining many underlying processes in models. For example, a recent study (Du and Rotunna

2014) suggests that spatially and diurnally varying surface heating and friction lead to the observed

diurnal variations in the Great Plain low-level jet (GPLLJ) with an early morning maximum. The diurnal

variations in the GPLLJ are further linked to daytime subsidence (nighttime ascent) over the Great Plains

and the opposite west of it due to local vorticity balance (Pu and Dickinson 2014). Thus, we can use the

diurnal amplitude (A) and phase (tmax) of the GPLLJ (i.e., meridional wind over the Great Plains, plus

precipitation diurnal cycle) as the diurnal metrics and apply them to diagnose the model processes

underlying the diurnal cycle over the Great Plains, including model-simulated surface heating and friction

that control the diurnal variations in the GPLLJ and the related vertical motion over the Great Plains. We

can also examine whether model resolution plays a role in this simulation. It may require climate models

to have a grid size finer than 100km to simulate the meridional and zonal variations in surface heating and

friction (and thus the GPLLJ) and the resultant regional diurnal circulation seen in the North American

Regional Reanalysis (NARR) (Fig. 4). For the GFDL AM4 and NCAR CAM5 models, we will have access

to model output with a grid size finer than 50km. Thus, we will be able to examine whether model

resolution is crucial for simulating the diurnal cycle in the central U.S.

The above examples illustrate the importance of the process-based understanding of the diurnal

variations. Without this knowledge, most diurnal metrics will not be very helpful to modelers. In this

project, we will not only provide the diurnal metrics and their observed values, but more importantly we

will also include the constructive information that links each of the diurnal metrics to specific underlying

physical processes, so that modelers would know where to look for deficiencies when a diurnal bias is

found.

13

FIG. 5: (a) Long-term (1981-2000) mean June-August (JJA) differences (

oC) between model-simulated

(GFDL prototype AM4 model) and observed (from PRISM) surface daily maximum air temperature

(Tmax). (b) Same as (a) but for surface daily minimum air temperature (Tmin).

3.3.4 Applying the diurnal metrics to diagnose problems in GFDL and other CMIP5 models

Our diurnal metrics and their linkages to specific underlying processes will be available to any

modelers. To demonstrate their usefulness, we will apply them to help diagnose problems in the GFDL

and other select CMIP5 models at the process-level. Besides the GFDL AM4 prototype model, we will

include at least the NCAR CESM (or CAM5) in this analysis. Other CMIP5 models may also be examined,

but the availability of high temporal-resolution data from these models may limit our choices. Our GFDL

collaborators will help us in this task by providing high temporal-resolution output from their model runs

and sharing their expertise in modeling the PBL, clouds, convection and precipitation. Due to the coarse

vertical resolution in upper oceans and daily coupling between the atmosphere and oceans, many coupled

models lack a diurnal cycle in sea surface temperatures (SSTs). This leads to a weak diurnal cycle over

the oceans (Dai and Trenberth, 2004; Danabasoglu et al., 2006) and other problems (Bernie et al., 2008).

As shown by Fig. 2 and Fig.5, the GFDL AM4 prototype model still has large biases in its Tmin,

Tmax and DTR over the US (and likely over other regions too). The warm bias in Tmin (Fig. 5b) suggests

that surface downward longwave (LWdn) radiation may be too high, which allows the surface to maintain

a high Tmin at night. The surface LWdn is controlled primarily by air temperature and water vapor

content and secondarily by cloud amount in the lower troposphere (Zhang et al. 1995). Since the surface

and lower tropospheric temperatures are tightly coupled with each other, to maintain a warm bias in these

temperatures the model has to have a strong greenhouse warming effect from the mid-upper troposphere

(otherwise, the lower troposphere will cool to a lower temperature). The most likely way to do that is

through an enhanced warming by excessive water vapor in the middle to upper troposphere, although

excessive mid-high level clouds could theoretically also enhance the warming on the surface and the

lower troposphere, but the Tmax biases (Fig. 5a) do not suggest a systematic bias in cloudiness across the

US. In fact, Fig. 5a suggests too few clouds over the central U.S. Thus, to diagnose the problems

associated with the biases in Tmin, we will start by examining the nighttime surface LWdn, lower- and

mid-upper tropospheric water vapor content (and the associated relative humidity and temperature), and

also cloud amount in the lower and mid-upper troposphere.

It quickly becomes clear that such a diagnostic analysis can be fairly complicated as many of the

processes are tightly coupled. For example, the nighttime warm biases at the surface and lower

troposphere will likely have an impact on the mid-upper tropospheric temperature and humidity fields,

thus the cause-and-effect relationship can become fuzzy. Nevertheless, there may be other deficient

physical processes (e.g., too much vertical transport of moisture by convection) that may lead to excessive

14

water vapor and nighttime clouds in the mid-upper troposphere, or an excessively high tropopause over

the US so that a higher surface temperature can be maintained as the height of the effective radiative

equilibrium temperature is raised. Thus, the bias in the Tmin metric can only point to an excessive

nighttime greenhouse effect. Diagnosing what causes this excessive greenhouse effect will require

additional knowledge and information. We hope to gain in-depth understanding of the major controlling

factors for Tmin through our empirical analysis in Task 3.3.2, and apply this knowledge to diagnose the

causes for the Tmin and other diurnal biases in the GFDL and other models.

Since a climate model has consistent internal physics, a bias in one variable (such as a warm Tmin)

will likely be associated with consistent biases in other related fields (e.g., warm T and high specific

humidity q in the lower troposphere at night, and large LWdn). The challenge is to find the initial cause(s)

that triggered this train of biased response. The diagnostic analysis can only point to some possible causes.

The final solution will depend on the results from model test runs and may vary from model to model. We

will work with our GFDL collaborators to make some test runs to see if our diagnosed causes can really

reduce the diurnal biases in Tmin and other fields. For example, if we found that nighttime water vapor in

the mid-upper troposphere is too excessive over the US in the GFDL AM4, then we will work together to

find a best way to reduce this water vapor bias in the model. This will be a non-trivial task as the mid-

upper tropospheric water vapor is controlled by different processes, such as convection, large-scale

circulation, etc. The key to success for this kind of work is a close collaboration between the people doing

the diagnostic work and the people doing the modeling work. In this project, we have set up a good team

with the participation of two leading modelers from GFDL who have a strong desire to improve their

model.

3.4 Relevance to the MAPP Competition and NOAA CPO's long-term goal

This proposal is for MAPP Competition - Process-oriented evaluation of climate and Earth system

models and derived projections (Area A, Type 2), which emphasizes projects to "develop and apply

process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and

observational bases". The diurnal cycle is a well-studied, fundamental feature of Earth's climate. The

focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to

underlying physical processes make our metrics truly process-oriented. We will also apply the new

diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this

proposal is directly responsive to the MAPP competition. Improving climate models and our

understanding of the diurnal cycle is also an important step to achieve NOAA's long-term climate goal to

"improved scientific understanding of the changing climate system and its impacts".

3.5 Work Plan

Year 1: To gather, quality-control, and compile a diurnal data set based on the data sources listed in

Table 1, quantify the diurnal variations in various fields (cf. Table 2) and start the analysis of the

underlying processes (Tasks 3.2.1 and 3.2.2);

Year 2: To complete the analysis of underlying physical processes, and then design and compute the

diurnal metrics and link them to specific physical processes (Task 3.2.3); and

Year 3: To apply the diurnal metrics to diagnose problems in GFDL AM4 and other CMIP5 models

(Task 3.2.4).

Comparisons with GFDL AM4 prototype model will be made from the start, so that some feedback

from this project will be available before GFDL freezes its next version of the AM4, although more

detailed diagnostic analyses will be in Year 3. Throughout the project, research results will be presented

at AMS, AGU and other conferences, and will be written up for publication.

15

Table 2. Variables, associated diurnal metrics, and their underlying physical processes to be analyzed.

Area

Variable: Diurnal Metric Underlying Physical Processes or factors

Thermal

heating

Surface daily maximum air temperature

(Tmax): TmaxTmean

Surface solar radiation: surface and cloud albedo

Surface evaporation: soil moisture, wind speed, RH

Surface daily minimum air temperature

(Tmin): TmeanTmin

Tmean = daily mean temperature

Surface downward LW radiation: temperature, water

vapor, and cloud amount in the lower troposphere, which

is related to the greenhouse warming from the mid-upper

troposphere.

DTR (=Tmax-Tmin): DTR All the processes under Tmax and Tmin

Surface pressure (Ps): the amplitude

(A=maximum - daily mean) and

phase of the diurnal (24-hr, S1) and

semidiurnal (12-hr, S2) tides

S1: surface sensible heating, tropospheric latent and solar

heating.

S2: stratospheric ozone heating, tropospheric latent and

solar heating, spurious wave reflection at model top.

Surface sensible (SH) and latent (LH)

heat fluxes: diurnal amplitude (A)

and local time of maximum (tmax)

SH: surface Ts-Ta gradient, turbulence mixing, near-

surface wind speed, PBL1 stability

LH: soil moisture content, vegetation type, surface vapor

deficit, wind speed, and radiative heating

T, q, & RH in PBL: diurnal amplitude

(A) and local time of maximum (tmax)

at a level inside the PBL1

Related to the diurnal variations in surface temperature

(Ts), SH flux, and turbulence mixing in the PBL.

PBL height: diurnal amplitude (A)

and local time of maximum (tmax)

Related to diurnal variations in Ts, SH flux, and

turbulence mixing in the PBL

Tropospheric

Conditions

Air temperature (T), specific humidity

(q), relative humidity (RH): diurnal

amplitude (A) and local time of

maximum (tmax) at 850, 500 and

200hPa

Lower troposphere: related to surface T, SH and LH

diurnal variations and the T and SH diurnal cycle inside

the PBL

Mid to upper troposphere: related to atmospheric

absorption of solar radiation, latent heating.

Large-scale vertical velocity ():

diurnal amplitude (A) and local time

of maximum (tmax) at 850, 500 and

200hPa averaged over the time

without convection

Related to large-scale diurnal circulation (e.g., sea

breezes) and differential surface heating over land (e.g.,

between the Rockies and the central Great Plains in the

US, Fig. 4). May enhance or suppress local convection

depending on its diurnal phase.

Near-surface winds and low-level

convergence: diurnal amplitude (A)

and local time of maximum (tmax) for

wind and convergence fields at 10m

and 850hPa

Surface winds: related to the diurnal cycle of the PBL

(downward mixing of momentum);

Low-level convergence: related to convection and large-

scale vertical motion (e.g., the Great Plain low-level jet).

Clouds,

Convection and

Precipitation

Warm-seasonal low-level cloud amount

and top height: diurnal amplitude (A)

and local time of maximum (tmax)

Related to shallow and deep cumulus convection, which

in turn is related to surface sensible and latent heating,

and lower-tropospheric instability, large-scale motion,

etc.

CAPE2, CIN

3 (negative buoyancy),

convective precipitation amount,

frequency and intensity: diurnal

amplitude (A) and local time of

maximum (tmax)

CAPE and CIN: related to surface sensible and latent

heating and convection

Convective P frequency: related to frequency of moist

convection or the onset threshold of deep convection

Convective P intensity: related to intensity of deep

convection or amount of CAPE it consumes. 1 PBL = the Planetary Boundary Layer, the lowest atmospheric layer (~1km) with well mixed T and q

profiles. 2 CAPE = Convective Available Potential Energy

3 CIN = Convective Inhibition (i.e., the negative buoyancy below the level of free convection)

16

3.6 Project Management and Collaborations

PI A. Dai will lead this project and be responsible for the implementation of the proposed work. He

will supervise the graduate students supported by this proposal and work with Co-PI J. Wang to carry out

most of the proposed work. Co-Is Chris Golaz and M. Zhao will share their expertise in studying and

modeling the PBL, clouds and convection during the diurnal analyses (Tasks 3.2.1-3.2.3), and provide

high temporal-resolution data from GFDL AM4 model simulations and help diagnose underlying

deficiencies in the GFDL model (Task 3.2.4).

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Wolff, 2007: The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear,

combined-sensor precipitation estimates at fine scales. J Hydrometeorol. 8, 38–55

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Johnson, R. H., P. E. Ciesielski, J. H. Ruppert, Jr., and M. Katsumata, 2014: Sounding-based

thermodynamic budgets for DYNAMO. J. Atmos. Sci., submitted.

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circulation using a cloud-resolving model as a superparameterization of physical processes. J. Atmos.

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Liang, X.-Z., L. Li, A. Dai, K. E. Kunkel, 2004: Regional climate model simulation of summer

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Lin, X., D.A. Randall, and L.D. Fowler, 2000: Diurnal variability of the hydrologic cycle and radiative

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versions of the atmospheric model in CESM1 using flux station observations. J. Clim., 26 26–44.

Lindvall, J. and G. Svensson, 2014: The diurnal temperature range in the CMIP5 models. Clim. Dyn. DOI

10.1007/s00382-014-2144-2, in press.

18

Menne, M.J., I, Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose,

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during boreal summer with a high-resolution GCM. Mon. Wea. Rev. 138, 3434-3453.

Pu, B. and R. E. Dickinson, 2014: Diurnal Spatial Variability of Great Plains Summer Precipitation

Related to the Dynamics of the Low-Level Jet. J. Atmos. Sci., 71, 1807–1817.

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general circulation model. J. Atmos. Sci., 48, 40–62.

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Soc. 80, 2261-2287.

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distribution of weather states indicated by distinct mesoscale cloud property mixtures. Geophys. Res.

Lett., 32, L21812, doi:10.1029/2005GL024584.

Sato, T., H. Miura, M. Satoh, Y. N. Takayabu, and Y. Q. Wang, 2009: Diurnal cycle of precipitation in

the Tropics simulated in a global cloud-resolving model. J. Clim., 22, 4809–4826.

Seidel, D. J., M. Free, and J. Wang, 2005: Diurnal cycle of upper-air temperature estimated from

radiosondes, J. Geophys. Res., 110, D09102, doi:10.1029/2004JD005526.

Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson, and B. Medeiros, 2012: Climatology of

the planetary boundary layer over the continental United States and Europe, J. Geophys. Res., 117,

D17106, doi:10.1029/2012JD018143.

Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P.

Gabriel, and J. Haynes, 2010: Dreary state of precipitation in global models, J. Geophys. Res., 115,

D24211, doi:10.1029/2010JD014532.

Stratton, R. A., and A. J. Stirling, 2012: Improving the diurnal cycle of convection in GCMs. Q. J. R.

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satellite observations. Geophys. Res. Lett., 40, 4464-4468, doi: 10.1002/grl.50874

Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design.

Bull. Amer. Met. Soc., 93, 485-498.

Wang, B., H. J. Kim, K. Kikuchi, and A. Kitoh, 2011: Diagnostic metrics for evaluation of annual and

diurnal cycles. Clim. Dyn., 37, 941–955.

Wang, J., H.L. Cole, D.J. Carlson, E.R. Miller, K. Beierle, A. Paukkunen, and T.K. Laine, 2002:

Corrections of humidity measurement errors from the Vaisala RS80 radiosonde–Application to

TOGA_COARE data. J. Atmos. Oceanic Technol., 19, 981-1002.

Wang, J., D. J. Carlson, D. B. Parsons, T. F. Hock, D. Lauritsen, H. L. Cole, K. Beierle and N.

Chamberlain, 2003: Performance of operational radiosonde humidity sensors in direct comparison

with a chilled mirror dew-point hygrometer and its climate implication. Geophy. Res. Lett., 30,

10.1029/2003GL016985.

Wang, J., L. Zhang, A. Dai, T. Van Hove, J. Van Baelen, 2007: A near-global, 2-hourly data set of

atmospheric precipitable water from ground-based GPS measurements. J. Geophys. Res., 112,

D11107. 10.1029/2006JD007529.

Wang, J. and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from

comparisons with ground-based GPS measurements. J. Climate, 21, 2218-2238.

Wang, J., and L. Zhang, 2009: Climate applications of a global, 2-hourly atmospheric precipitable water

dataset from IGS ground-based GPS measurements. J. Geodesy, 83, 209-217.

Wang, J., L. Zhang, P.-H. Lin, Mark Bradford, Harold Cole, Jack Fox, Terry Hock, Dean Lauritsen, Scot

Loehrer, Charlie Martin, Joseph VanAndel, Chun-Hsiung Weng and Kathryn Young, 2010: Water

vapor variability and comparisons in subtropical Pacific from T-PARC Driftsonde, COSMIC and

reanalyses. J. Geophys. Res., 115, D21108, doi:10.1029/2010JD014494.

19

Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer and H. Voemel, 2013a: Radiation dry bias correction

of Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic

Technol., 30, 197-214.

Wang, J., T. Hock, S. A. Cohn, C. Martin, N. Potts, T. Reale, B. Sun and F. Tilley, 2013b: Unprecedented

upper air dropsonde observations over Antarctica from the 2010 Concordiasi Experiment: Validation

of satellite-retrieved temperature profiles. Geophys. Res. Lett., 40, DOI: 10.1002/grl.50246.

Wang, J., A. Dai, C. Mears and L. Zhang, 2014: Global water vapor trend and its diurnal asymmetry

based on GPS, radiosonde and microwave satellite measurements. J. Geophys. Res., to be submitted.

Ware, R.H. D. W. Fulker, S. A. Stein, D. N. Anderson, S. K. Avery, R. D. Clark, K. Droegemeier, J. P.

Kuettner, and J. B. Minster, 2000: Suominet: A real-time national GPS network for atmospheric

research and education. Bull. Amer. Meteor. Soc. 81, 677-694.

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Experiment. Bull. Amer. Meteor. Soc., 73, 1377-1416.

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Geerts, J. Wang and W. F. Feltz, 2004: An overview of the international H20 project (IHOP_2002)

and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253-277.

Yoneyama, K. C. Zhang, and C.N. Long, 2013: Tracking Pulses of the Madden–Julian Oscillation. Bull.

Amer. Meteor. Soc., 94, 1871–1891.

Xie, S. and coauthors, 2010: Clouds and more: ARM Climate Modeling Best Estimate Data. Bull. Amer.

Meteor. Soc., 91, 13–20.

Zhang, Y., D.J. Seidel, J.-C. Golaz, C. Deser, and R. A. Tomas, 2011: Climatological characteristics of

Arctic and Antarctic surface-based inversions. J. Climate, 24, 5167–5186.

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radiative fluxes from physical quantities based on ISCCP data sets. Part 1: Methods and sensitivity to

input data uncertainties. J. Geophys. Res., 100, 1149–1165.

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interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate,

22, 6653–6678, doi:10.1175/2009JCLI3049.1.

Zhao, M. 2014: An investigation of the connections among convection, clouds, and climate sensitivity in

a global climate model. J. Climate, 27, 1845–1862.

Zhao, T., A. Dai, and J. Wang, 2012: Long-term trend of upper-air humidity over China from

homogenized radiosonde data. J. Climate, 25, 4549-4567.

4. Data/Information Sharing Plan:

The results and data produced in this project, including the diurnal data set, the diurnal

metrics data, and the information regarding the underlying physical processed, will be made

available to the public through presentations at meetings, publications in journals, and online

postings. In particular, we will work with any modeling groups for them to include our diurnal

metrics and related data sets into their model evaluation packages.

24

6. Curriculum Vitae:

Biographical Sketch Aiguo Dai Associate Professor

Depart. of Atmos. and Environ. Sci., University at Albany, SUNY

Tel. 518-442-4474; email: adai@albany.edu

A. Professional Preparation: Postdoc: 1997-98, Climate & Global Change, NCAR, Boulder, CO

Ph.D.: 1996, Atmospheric Science, Columbia University, NY, NY

M.S.: 1988, Atmospheric Science, Chinese Academy of Sci., Beijing, China

B.S.: 1985, Meteorology, Nanjing University, Nanjing, China

B. Appointments: 8/2012-present: Associate Professor, Dept of Atmos. & Environ. Sci., SUNY, Albany, NY

7/2008-7/2012: Scientist III, CGD, NCAR, Boulder

9/2004-6/2008: Scientist II, CGD, NCAR, Boulder

1999-8/2004: Project Scientist, CGD, NCAR, Boulder, CO.

1997-1998: NOAA/UCAR Postdoctoral Scientist at NCAR, Boulder, CO.

C. Synergistic Activities (selected):

Editor, J. Climate, January 2011-present

Chair, AMS Committee on Climate Variability and Change, January 2011-February 2014

Member, AMS Committee on Climate Variability and Change, Feb. 2006 – Jan. 2011

Associate Editor, J. Hydrology, January 2010-present

Associate Editor, Advances in Atmospheric Sciences, Oct. 2009-August 2011

Chair, AMS 20th Conf. on Climate Variability and Change, 20–24 January 2008, New Orleans

Contributing Author, the 3rd

, 4th and 5

th IPCC Assessment Reports

D. Journal Publications Since 2011 (see www.cgd.ucar.edu/cas/adai/pub.html for links to pdf files)

Total citations 10,839, H index=49 as of October 2014 based on Google Scholar. One of the ~3200

highly cited researchers in all fields (one of the 159 in Geoscience) in the world.

26. Dong, B., and A. Dai, 2014: The influence of the Inter-decadal Pacific Oscillation on temperature and

precipitation over the globe. Climate Dynamics, being revised.

25. Hegerl, G.C., and 26 Co-authors (including A. Dai), 2014: Challenges in quantifying changes in the global

water cycle. Bull. Am. Met. Soc., accepted.

24. Zhao, T., and A. Dai, 2014: The magnitude and causes of global drought changes in the 21st century under

a moderate emissions scenario. J. Climate, being revised.

23. Dai, A., J.C. Fyfe, S.-P. Xie, and X. Dai, 2014: Decadal modulation of global-mean temperature by

internal climate variability. Nature Climate Change, under review.

22. Luo, D., Y. Yao*, and A. Dai, 2014: Decadal relationship between the European blocking and North

Atlantic Oscillation. Part I: Atlantic conditions. J. Atmos. Sci., accepted with minor revision.

21. Luo, D., Y. Yao*, and A. Dai, 2014: Decadal relationship between the European blocking and North

Atlantic Oscillation. Part II: A theoretical model study. J. Atmos. Sci., accepted with minor.

20. Covey, C., A. Dai, R. S. Lindzen, and D. Marsh, 2014: Atmospheric tides in the latest generation of

climate models. J. Atmos. Sci., 71, 1905-1913.

19. Rasmussen, R., K. Ikeda, C. Liu, D. Gochis, M. Clark, A. Dai, E. Gutmann, J. Dudhia, F. Chen, M.

Barlage, and D. Yates, 2014: Impacts of climate change on the water balance of the Colorado headwaters:

High resolution regional climate model simulations. J. Hydrometeorol., doi: 10.1175/JHM-D-13-0118.1.

18. Luo, D., J. Cha*, L. Zhong*, and A. Dai, 2014: A nonlinear multi-scale interaction model for atmospheric blocking: The eddy-blocking matching mechanism. Q. J. Roy.Meteorol. Soc., DOI:10.1002/qj.2337.

17. Trenberth, K.E., A. Dai, G. van der Schrier, P.D. Jones, J. Barichivich*, K.R. Briffa, and J. Sheffield,

2014: Global warming and changes in drought. Nature Climate Change, 4, 17-22.

25

16. Dai, A., H. Li, Y. Sun, L.-C. Hong*, LinHo, C. Chou, and T. Zhou, 2013: The relative roles of upper and

lower tropospheric thermal contrasts and tropical influences in driving Asian summer monsoons. J . Geophys. Res., 118: 7024-7045, doi:10.1002/jgrd.50565.

15. Jia, B., Z. Xie, A. Dai, C. Shi, and F. Chen*, 2013: Evaluation of satellite and reanalysis products of

downward surface solar radiation over East Asia: Spatial and seasonal variations. J. Geophys. Res.-

Atmospheres, 118: doi:10.1002/jgrd.50353.

14. Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer, and H. Vömel, 2013: Radiation dry bias correction of

Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic Tech. 30:

197-214.

13. Dai, A., 2013: The Influence of the Inter-decadal Pacific Oscillation on U.S. precipitation during 1923-

2010. Climate Dynamics, 41: 633-646, doi:10.1007/s00382-012-1446-5.

12. Dai, A., 2013: Increasing drought under global warming in observations and models. Nature Climate Change, 3: 52-58.

11. Shiu, C.-J., S.C. Liu, S.C, C. Fu, A. Dai and Y. Sun, 2012: How much do precipitation extremes change in

a warming climate? Geophys. Res. Lett. 39, L17707, doi:10.1029/2012GL052762.

10. Dunn, R.J.H., K.M. Willett, P.W. Thorne, E. V. Woolley, I. Durre, A. Dai, D. E. Parker, and R.S. Vose,

2012: HadISD: A Quality Controlled global synoptic report database for selected variables at long-term

stations from 1973-2010. Climates of the Past. 8, 1649–1678, doi: 10.5194/cp-8-1649-2012.

9. Bacmeister, J. T., P. H. Lauritzen, A. Dai and J. E. Truesdale, 2012: Assessing possible dynamical effects of

condensate in high resolution climate simulations. Geophys. Res. Lett., 39, L04806,

doi:10.1029/2011GL050533.

8. Zhao, T., A. Dai, and J. Wang, 2012: Trends in tropospheric humidity from 1970-2008 over China from a

homogenized radiosonde dataset. J. Climate, 25: 4549-4567.

7. Dai, A., 2011: Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI)

during 1900-2008. J. Geophys. Res., 116, D12115, doi:10.1029/2010JD015541.

6. Willett, K., A. Dai, and D. Berry, 2011: Surface humidity. In: State of the Climate in 2010. Bull. Amer.

Meteorol. Soc., 92, S40-S41.

5. Wang, L., Q. Huang, A. Dai, Z. Guan, J. He, and Z. Wu, 2011: Inhomogeneous distributions of Meiyu

rainfall in the Jiang-Huai basin, and associated circulation patterns. Climate Res., 50, 203-214.

4. Monahan, A., Y. He, N. McFarlane, and A. Dai, 2011: The probability distribution of land surface wind

speeds. J. Climate, 24, 3892-3909.

3. Dai, A., J. Wang, P.W. Thorne, D.E. Parker, L. Haimberger, and X.L. Wang, 2011: A new approach to

homogenize daily radiosonde humidity data. J. Climate, 24, 965-991.

2. Covey, C., A. Dai, D. March, and R. S. Lindzen, 2011: The surface-pressure signature of atmospheric tides

in modern climate models. J. Atmos. Sci., 68, 495-514.

1. Dai, A., 2011: Drought under global warming: A review. Wiley Interdisciplinary Reviews: Climate Change,

2, 45-65.

Five other relevant papers:

Dai, A., X. Lin, and K.-L. Hsu, 2007: The frequency, intensity, and diurnal cycle of precipitation in surface

and satellite observations over low- and mid-latitudes. Climate Dynamics, 29, 727-744.

Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 4605-4630.

Dai, A., and K. E. Trenberth, 2004: The diurnal cycle and its depiction in the Community Climate System

Model. J. Climate, 17, 930-951.

Dai, A., J. Wang, R. H. Ware, and T. Van Hove, 2002: Diurnal variation in water vapor over North America

and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107(D10), 4090,

10.1029/2001JD000642.

Dai, A., K. E. Trenberth, and T. R. Karl, 1999: Effects of clouds, soil moisture, precipitation and water vapor

on diurnal temperature range. J. Climate, 12, 24512473.

26

Biographical Sketch – Junhong (June) Wang, Ph.D. Research Associate Professor

Department of Atmospheric and Environmental Sciences

University at Albany, SUNY

1400 Washington Avenue, Albany, NY 12222

Tel. 518-442-3478; email: jwang20@albany.edu

A. Professional Preparation Peking University, Beijing, China Atmospheric Science B.S. 1987

NRCMEF, Beijing, China Atmospheric Science M.S. 1990

(National Research Center for Marine Environment Forecasts)

Columbia University, New York Atmospheric Science Ph.D. 1997

University of Colorado, Boulder, CO Atmospheric Science PostDoc 1997-1998

B. Appointments

2012-present Research Associate Professor, Department of Atmospheric & Environmental

Sciences University at Albany, SUNY, Albany, NY

2008-2014 Scientist III, Earth Observing Laboratory (EOL), NCAR, Boulder, CO

6/2009-8/2009 Visiting Scientist, U.K. Met Office Hadley Centre, Exeter, U.K.

2005-2008 Scientist II, EOL, NCAR, Boulder, CO

2002-2005 Scientist I, Atmospheric Technology Division (ATD), NCAR, Boulder, CO

1999-2001 Associate Scientist, ATD, NCAR, Boulder, CO

1997-1998 Postdoctoral Research Scientist, University of Colorado, Boulder, CO

1991-1996 Graduate Research Assistant, NASA Goddard Institute for Space Studies (GISS), NY

1993-1994 Teaching Assistant for Atmospheric & Climate Courses, Columbia Univ.

6/1991-9/1991 Research Assistant, NASA GISS, New York

7/1990-12/1990 Research Associate, NRCMEF/Peking University, China

1988-1990 Graduate Research Assistant, NRCMEF/Peking Univ., China

C. Publications

i) Publications of the last 3 years: 1. Mears, C. A., J. Wang, D. Smith and F. J. Wentz, 2014: Intercomparison of total precipitable water

measurements made by satellite borne microwave radiometers and ground-based GPS instruments. J. Geophys. Res., submitted.

2. Boylan, P., J. Wang, S. Cohn, E. Fetzer, E. S. Maddy, and S. Wong, 2014: Validation of AIRS version 6

temperature profiles and surface-based inversions over Antarctica using Concordiasi dropsonde data. J.

Geophys. Res., submitted.

3. Wang, J., K. Young, T. Hock, D. Lauritsen, D. Behringer, M. Black, P. G. Black, J. Franklin, J.

Halverson, J. Molinari, L. Nguyen, T. Reale, J. Smith, B. Sun, Q. Wang and J. Zhang, 2014: A long-term,

high-quality, high vertical resolution GPS dropsonde dataset for hurricane and other studies. Bull. Amer. Meteor. Soc., accepted.

4. Mears, C, S. Ho, L. Peng and J. Wang, 2014: Total column water vapor, in State of the Climate in 2013.

Bull. Amer. Meteorol. Soc., 95, S20-21.

5. Yu, H., P.E. Ciesielski, J. Wang, H.-C. Kuo, H. Voemel and R. Dirksen, 2014: Evaluation of humidity

correction methods for Vaisala RS92 tropical sounding data. J. Atmos. Oceanic Technol., submitted.

6. Intrieri, J.M., G. de Boer, M.D. Shupe, J.R. Spackman, J. Wang, P.J. Neiman, G.A. Wick, T.F. Hock, and

R.E. Hood, 2014: Global Hawk dropsonde observations of the Arctic atmosphere during the Winter

Storms and Pacific Atmospheric Rivers (WISPAR) field campaign. Atmospheric Measurement Techniques, revised.

7. Bock, O., P. Willis, J. Wang and C. Mears, 2014: A high-quality, consistent, global, long-term (1993-

2008) DORIS precipitable water dataset for climate monitoring and model verification. J. Geophys. Res., accepted.

27

8. Ciesielski, P. E., H. Yu, R. H. Johnson, K. Yoneyama, M. Katsumata, C. N. Long, J. Wang and others,

2014: Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE: Development and

corrections. J. Atmos. Oceanic Technol., 31, 741-764.

9. Mears, C., J. Wang, S. Ho, and L. Zhang, 2013: Total column water vapor, in State of the Climate in

2012. Bull. Amer. Meteorol. Soc., 94, S20-21.

10. Wang, J., T. Hock, S. A. Cohn, C. Martin, N. Potts, T. Reale, B. Sun and F. Tilley, 2013b: Unprecedented

upper air dropsonde observations over Antarctica from the 2010 Concordiasi experiment: Validation of

satellite-retrieved temperature profiles. Geophys. Res. Lett., 40, DOI: 10.1002/grl.50246.

11. Cohn, S. A., T. Hock, P. Cocquerez, J. Wang and others, 2013: Driftsondes: Providing in-situ long-

duration dropsonde observations over remote regions. Bull. Amer. Meteor. Soc., 94, 10.1175/BAMS-D-12-00075.1.

12. Wang, J., L. Zhang, A. Dai, F. Immler, M. Sommer and H. Voemel, 2013a: Radiation dry bias correction

of Vaisala RS92 humidity data and its impacts on historical radiosonde data. J. Atmos. Oceanic Technol.,

30, 197-214.

13. Rabier, F., S. Cohn, P. Cocquerez, A. Hertzog, L. Avallone, T. Deshler, J. Haase, T. Hock, A.

Doerenbecher, J. Wang, and others, 2013: The Concordiasi field experiment over Antarctica: first results

from innovative atmospheric measurements, Bull. Amer. Meteor. Soc., 94, DOI:10.1175/BAMS-D-12-

00005.1.

14. Mears, C., J. Wang, S. Ho, L. Zhang and X. Zhou, 2012: Total column water vapor, in State of the

Climate in 2011. Bull. Amer. Meteorol. Soc., 93, S25-S26.

15. Zhao, T., A. Dai, and J. Wang, 2012: Long-term trend of upper-air humidity over China from

homogenized radiosonde data. J. Climate, 25, 4549-4567.

16. Ciesielski, P. E., P. T. Haertel, R. H. Johnson, J. Wang, and S. M. Loehrer, 2012: Developing high-quality

field program sounding datasets. Bull. Amer. Meteorol. Soc., 93, 325–336.

17. Mears, C., J. Wang, S. Ho, L. Zhang and X. Zhou, 2011: Total column water vapor, in State of the

Climate in 2010. Bull. Amer. Meteorol. Soc., 92 (6), S41-S42.

18. Dai, A., J. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. Wang, 2011: A new approach to

homogenize daily radiosonde humidity data J. Climate, 24, 965-991.

ii) Five other relevant papers: 1. Wang, J., and L. Zhang, 2009: Climate applications of a global, 2-hourly atmospheric precipitable water

dataset from IGS ground-based GPS measurements. J. of Geodesy, 83, 209-217.

2. Wang, J. and L. Zhang, 2008: Systematic errors in global radiosonde precipitable water data from

comparisons with ground-based GPS measurements. J. Climate, 21, 2218-2238.

3. Wang, J., L. Zhang, A. Dai, T. Van Hove, J. Van Baelen, 2007: A near-global, 2-hourly data set of

atmospheric precipitable water from ground-based GPS measurements. Journal of Geophysical Research,

112, D11107. 10.1029/2006JD007529.

4. Seidel, D. J., M. Free and J. Wang, 2005: The diurnal cycle of temperature in the free atmosphere

estimated from radiosondes. J. Geophys. Res., 110, D09102, doi:10.1029/2004JD005526.

5. Dai, A., J. Wang, R. H. Ware, and T. M. Van Hove, 2002: Diurnal variation in atmospheric water vapor

over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res.,

107(D10), 10.1029/2001JD000642.

D. Synergistic Activities (five examples)

• 2011-present: Member of AMS Board on Data Stewardship.

• 2010-present: Co-Chair for GRUAN task team on ground-based GNSS precipitable water (GNSS-PW)

observations.

• 2006-present: Member of Working Group on Atmospheric Reference Observations of GCOS

Atmospheric Observation Panel for Climate.

• 2005-2009: Editor for Journal of Atmospheric and Oceanic Technology.

• 2005: Member of NRC’s committee on reviewing US CCSP's first report about "Temperature trends in

the lower atmosphere: steps for understanding and reconciling differences"

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Biographical Sketch – Jean-Christophe “Chris” Golaz

Physical Scientist

NOAA Geophysical Fluid Dynamics Laboratory

201 Forrestal Rd ● Princeton, NJ 08540

Relevant experience

Chris Golaz is currently co-lead of the GFDL Atmospheric Working Group in charge of

developing the next generation GFDL atmospheric model (AM4). He is experienced in the

development of cloud parameterizations, including their implementation and evaluation in global

climate models.

Education

B.S. Physics, Swiss Federal Institute of Technology, Lausanne, Switzerland.

M.S. Atmospheric Science, Colorado State University.

PhD. Atmospheric Science, Colorado State University.

Selected publications

Guo, H., J.-C. Golaz, L. J. Donner, P. Ginoux and R. S. Hemler, 2014: Multivariate probability

density functions with dynamics in the GFDL atmospheric General Circulation Model:

Global tests. J. Clim., 27, 2087-2108, doi: 10.1175/JCLI-D-13-00347.1

Zhang, M. and co-authors, 2013: CGILS: Results from the first phase of an international project

to understand the physical mechanisms of low cloud feedbacks in single column models. J.

Adv. Model. Earth Syst., 5, 826-842, doi: 10.1002/2013MS000246

Suzuki, K., J.-C. Golaz and G. L. Stephens, 2013: Evaluating cloud tuning in a climate model

with satellite observations. Geophys. Res. Lett., 40, 4464-4468, doi: 10.1002/grl.50874

Lin, Y., M. Zhao, Y. Ming, J.-C. Golaz, L. J. Donner, S. A. Klein, V. Ramaswamy and S. Xie,

2013: Precipitation partitioning, tropical clouds, and intraseasonal variability in GFDL

AM2. J. Climate, 26, 5453-5466, doi: 10.1175/JCLI-D-12-00442.1

Golaz, J.-C., L. W. Horowitz and H. Levy II, 2013: Cloud tuning in a coupled climate model:

impact on 20th century warming. Geophys. Res. Lett., 40, 1-6, doi: 10.1002/grl.50232

Levy II, H., L. W. Horowitz, D. M. Schwarzkopf, Y. Ming, J.-C. Golaz, V. Naik and V.

Ramaswamy, 2013: The roles of aerosol direct and indirect effects in past and future climate

change. J. Geophys. Res., 118, 1-12, doi: 10.1002/jgrd.50192

Huang, J., E. Bou-Zeid and J.-C. Golaz, 2013: Turbulence and vertical fluxes in the stable

atmospheric boundary layer. Part II: a novel mixing-length model. J. Atmos. Sci., 70, 1528-

1542, doi: 10.1175/JAS-D-12-0168.1

Zhang, M. H., C. S. Bretherton, P. N. Blossey, S. Bony, F. Brient and J.-C. Golaz, 2012: The

CGILS experimental design to investigate low cloud feedbacks in general circulation models

by using single-column and large-eddy simulation models. J. Adv. Model. Earth Syst., 4,

M12001, doi: 10.1029/2012MS000182

Seidel, D. J., Y. Zhang, A. Beljaars, J.-C. Golaz, A. R. Jacobson, B. Medeiros, 2012:

Climatology of the planetary boundary layer over the continental United States and Europe.

J. Geophys. Res., 117, D17106, doi: 10.1029/2012JD018143.

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Zhang, Y., D. J. Seidel, J.-C. Golaz, C. Deser, and R. A. Tomas, 2011: Climatological

characteristics of Arctic and Antarctic surface-based inversions. J. Climate, 24, 5167-5186,

doi: 10.1175/2011JCLI4004.1.

Golaz, J.-C., M. Salzmann, L. J. Donner, L. W. Horowitz, Y. Ming, and M. Zhao, 2011:

Sensitivity of the aerosol indirect effect to subgrid variability in the cloud parameterization

of the GFDL Atmosphere General Circulation Model AM3. J. Climate, 24, 3145-3160, doi:

10.1175/2010JCLI3945.1.

Guo, H., J.-C. Golaz, and L. J. Donner, 2011: Aerosol effects on stratocumulus water paths in a

PDF-based parameterization. Geophys. Res. Lett., 38, L17808, doi: 10.1029/2011GL048611.

Donner, L. J. and co-authors, 2011: The dynamical core, physical parameterizations, and basic

simulation characteristics of the atmospheric component of the GFDL Global Coupled

Model CM3. J. Climate, 24, 3484-3519, doi: 10.1175/2011JCLI3955.1.

Stephens, G. L., T. L'Ecuyer, R. Forbes, A. Gettelmen, J.-C. Golaz, A. Bodas-Salcedo, K.

Suzuki, P. Gabriel and J. Haynes, 2010: Dreary state of precipitation in global models. J.

Geophys. Res, 115, D24211, doi: 10.1029/2010JD014532.

Guo, H., J.-C. Golaz, L. J. Donner, V. E. Larson, D. P. Schanen and B. M. Griffin, 2010: Multi-

variate probability density functions with dynamics for cloud droplet activation in large-

scale models: single column tests. Geosci. Model Dev., 3, 475-486, doi: 10.5194/gmd-3-

475-2010

Salzmann, M., Y. Ming, J.-C. Golaz, P. A. Ginoux, H. Morrisson, A. Gettelman, M. Krämer and

L. J. Donner, 2010: Two-moment bulk stratiform cloud microphysics in the GFDL AM3

GCM: description, evaluation, and sensitivity tests. Atmos. Chem. Phys., 10, 8037-8064, doi:

10.5194/acp-10-8037-2010

Xie, S., R. B. McCoy, S. A. Klein, R. T. Cederwall, W. J. Wiscombe, E. E. Clothiaux, K. L.

Gaustad, J.-C. Golaz, S. D. Hall, M. P. Jensen, K. L. Johnson, Y. Lin, C. N. Long, J. H.

Mather, R. A. McCord, S. A. McFarlane, G. Palanisamy, Y. Shi, and D. D. Turner, 2010:

CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data. Bull. Amer. Meteor.

Soc., 91, 1, 13-20, doi: 10.1175/2009BAMS2891.1

30

Ming Zhao CURRICULUM VITAE

Project Scientist III, University Corporation for Atmospheric Research

Geophysical Fluid Dynamics Laboratory / NOAA

Princeton University Forrestal Campus / 201 Forrestal Rd, Princeton, NJ 08540

Research Interests

Moist convection, clouds, atmospheric boundary layer turbulence and their

parameterizations in global climate models

Tropical cyclone and climate

Convection-clouds-climate-connections and feedback

Weather and climate extremes in changing climate

Education

Ph.D. in Atmospheric Sciences, University of British Columbia, Canada, 2003

M.Sc. in Atmospheric Remote Sensing, Nanjing Institute of Meteorology, China, 1993

B.Sc. in Atmospheric Physics, Nanjing Institute of Meteorology, China, 1990

Relevant Publications

1. Xiang, B., S-J Lin, M. Zhao, S. Zhang, G. Vecchi, T. Li, X. Jiang, L. Harris, J-H Chen,

2014: Beyond Weather Time Scale Prediction for Hurricane Sandy and Super Typhoon

Haiyan in a Global Climate Model. Mon. Wea. Rev. In Press.

2. Camargo, S.J., M.K. Tippett, A.H. Sobel, G.A. Vecchi, M. Zhao and I.M. Held, 2014:

Testing the performance of tropical cyclone genesis indices in future climates using the

HIRAM model. J. Climate. In Press.

3. Zhang, S., M. Zhao, S-J Lin, X. Yang and W. Anderson, 2014: Retrieval of Tropical

Cyclone Statistics with a High-Resolution Coupled Model and Data. Geophys. Res. Lett.,

41(2), DOI:10.1002/2013GL058879.

4. Zhao, M, 2014: An Investigation of the Connections between Convection, Clouds and

Climate Sensitivity in a Global Climate Model. J. Climate. 27, 1845-1862.

5. Jiang, X., M. Zhao and D. E. Waliser, 2012: Modulation of Tropical Cyclones Over the

Eastern Pacific by the Intra-seasonal Variability Simulated in an AGCM, J. Climate. 25,

6524-6538.

6. Zhao, M., I. M. Held and S-J Lin, 2012: Some Counter-Intuitive Dependencies of

Tropical Cyclone Frequency on Parameters in a GCM. J. Atmos. Sci., 69, 2272-2283.

7. Jiang, X., B.E., Waliser, D. Kim, M. Zhao, M. Khairoutdinov, W. Stern, S.D. Schubert,

K.R. Sperber, G.J. Zhang, W. Wang, R. Neale and M-I Lee, 2012: Simulation of the

Intraseasonal Variability over the Eastern Pacific ITCZ in Climate Models. Climate

Dynamics. 39, 617-636.

8. Zhao, M. and I. M. Held, 2012: TC-permitting GCM Simulations of Hurricane

Frequency Response to Sea Surface Temperature Anomalies Projected for the Late 21st

Century. J. Climate. 25, 2995-3009.

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9. Donner, L.J., B.L. Wyman, R.S. Hemler, L.W. Horowitz, Y. Ming, M. Zhao and 20 co-

authors, 2011: The Dynamical Core, Physical Parameterizations, and Basic Simulation

Characteristics of the Atmospheric Component of the GFDL Global Coupled Model

CM3. J. Climate, 24, 3484-3519.

10. Vecchi, G.A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I.M. Held and R.

Gudgel, 2011: Statistical-Dynamical Predictions of Seasonal North Atlantic Hurricane

Activity. Mon. Wea. Rev., 139, 1070-1082

11. Zhao, M. and I. M. Held, 2010: An Analysis of the Effect of Global Warming on the

Intensity of Atlantic Hurricanes Using a GCM with Statistical Refinement. J. Climate,

23, 6382–6393.

12. Zhao, M., I.M. Held and G.A. Vecchi, 2010: Retrospective Forecasts of the Hurricane

Season Using a Global Atmospheric Model Assuming Persistence of SST Anomalies.

Mon. Wea. Rev., 138, 3858-3868.

13. Zhao, M., I.M. Held, S-J. Lin, and G.A. Vecchi, 2009: Simulations of Global Hurricane

Climatology, Interannual Variability, and Response to Global Warming Using a 50km

Resolution GCM. J. Climate, 33, 6653-6678.