NAME Modeling and Data Assimilation “White Paper”

June 2003• Provides a strategy for accelerating progress on the fundamental modeling issues pertaining to the NAME science objectives

• Unveiled at NAME Modeling and Data Assimilation Workshop (UMD, June 03)

• Reviewed by the US CLIVAR Pan American Panel.

• Emphasizes activities that bring observationalists, modelers and physical parameterization experts together to focus on key physical processes that are deficient in coupled models.

     

 

NAME Modeling and Data Assimilation:

A Strategic Overview

NAME Science Working Group*

June 2003

IMPROVE warm season prediction

• Improve understanding and prediction of the life cycle of the North American monsoon system and its variability.

warm season convective processes in complex terrain; (Tier 1)

intraseasonal variability of the monsoon;(Tier 2) the response of warm season atmospheric

circulation and precipitation patterns to slowly varying, potentially predictable oceanic and continental surface conditions (Tier 3)

Strategy

I. Multi-scale Model Development

II. Multi-tier Synthesis and Data Assimilation

III. Prediction and Global-scale Linkages

GUIDING PRINCIPALS

The strategy must take maximum advantage of NAME enhanced observations, and should simultaneously provide model-based guidance to the evolving multi-tiered NAME observing program.

The modeling activities must maintain a multi-scale approach in which local processes are embedded in, and are fully coupled with, larger-scale dynamics.

NAMAPModel Assessment for the North American Monsoon

Experiment

D.S. Gutzler H.-K. Kim University of New Mexico NOAA/NCEP/CPC

gutzler@unm.edu hyun-kyung.kim@noaa.gov

Thanks to:CPC for hosting DG’s visit, Spring 2003NAMAP modeling participants

UCAR/JOSS for archiving NAMAP output

NAMAP Accomplishments

• Establish the baseline simulations/forecasts

To know what we do not know:

a) Position and structure of the GCLLJ

b) Diurnal cycle of the GCLLJ

c) Detailed structure and distribution

of rainfall (both in space and time)

d) Oceanic influence—local and remote

No obs here! What is the “true” diurnal cycle? All models show convective max between 21Z-04Z Different diurnal max over different places

use the NAME data

• Understand the dynamical processes related to NAME• Better monitoring of the monsoon systems and the warm season precipitation regimes over

North and Central America• Verify model forecasts • Improve modeling the physical processes related to the NAMEImprove the operational forecasts and applications

I. Multi-scale Model Development

Premise of the NAME modeling strategy is that deficiencies in our ability to model "local" processes are among the leading factors limiting forecast skill in the NAME region.

Requires: -improvements to the physical parameterizations -improvements to how we model interactions between local processes and the larger scales

I. Multi-scale Model Development

NAME Focus: Tier I

moist convection in the presence of complex terrain;Diurnal cycle land/atmosphere &ocean atmosphere interactions in the presence of complex terrainWe will have the NAME data as guide

“Bottom-up” approaches:

1.1. Multi-scale modeling Multi-scale modeling ->->

M. MoncrieffM. Moncrieff

Cloud-system-resolving models having computational domain(s) large enough to represent interaction/feedback with large scales

Multiscale models explicitly represent convective cloud systems

Computational domains

igure 6. The three-domain decomposition used in the MM5 experiment: the horizontal resolutions are 81-km, 27-km and 9-km, respectively. Domain 4 is the CSRM domain (3-km grid spacing).

Cloud-resolving domain ( )

81 km

9 km

27 km

2 kmM. Moncrieff

“top-down” approaches:

2. Global/regional models 2. Global/regional models

S.S. Schubert et al.; G. ZhangSchubert et al.; G. Zhang

Use the observations to determinea) Resolutionb) test the current parameterizations in the presence of

complex terrain, and larger-scale organizationc) E. g. Different convection schemesd) Radiation-cloud interaction

II. Multi-tier Synthesis and Data Assimilation

Data assimilation is critical to enhancing the value and extending the impact of the Tier I observations

The specific objectives are:

To better understand and simulate the various components of the NAM and their interactions with the larger-scales

To quantify the impact of the NAME observations

To identify model errors and attribute them to the underlying model deficiencies

Regional CDAS (R-CDAS) and NAME Data Impact and Prediction ExperimentsKingtse Mo and Wayne Higgins –CPC/NCEP, Fedor Mesinger--- UCAR/EMC,Hugo Berbery--- University of Maryland

1) Real time monitoring of hydro-meteorological conditions during NAME 2004 based on regional reanalysis and RCDAS;

2) Data impact studies

A) With data into the GTS system , data assimilation

will be done using CDAS (T62), GDAS( GFS T256) and R_CDAS relatively quickly

b) Same as (a) but without data from NAME

c) After 12 to 18 months, all data are collected including rain gauges, a final sets of data assimilation will be done using GDAS and RCDAS

d) forecasts (1-90 days) every 6h using GFS T126

All PIs, please help us

Please give me a list of

• A) station WMO ID

• B) lat-lon position

• C) Data type and time

For all data entering the GTS network before

the cutoff time h+16Z

Thanks

An Assessment and Analysis of the Warm Season Diurnal Cycle over the Continental US/N. Mexico in Global AGCM’S

Siegfried Schubert, Max Suarez, Myong-In Lee -NASA/GSFC Isaac Held-GFDL Arun Kumar, Hyun-Kyung Kim, Wayne Higgins – NCEP/CPC

http://janus.gsfc.nasa.gov/~milee/diurnal

OBJECTIVES1) Assess / analyze the diurnal cycle in three different AGCMs

(NASA, NCEP and GFDL),

2) Improve understanding of the important physical processes that drive the diurnal cycle,

3) Provide guidance for the development of physical parameterizations aimed at improving the simulation of the warm season hydrological cycle over the US / N. Mexico

III. Prediction and Global-Scale Linkages

Once we have a reliable model: we are able to

determine the predictability and prediction skill over the NAMS region associated with the leading patterns of climate variability;

Extend to examine the precipitation regimes over North and Central America determine the predictability and prediction skill associated with anomalous land surface conditions in the NAME region (e.g. soil moisture)assess the relative influences of local and remote SST’s

Predictability and Forecast Skill In Global Models

Objectives: 1) To examine the predictability of warm season precipitation over the NAM region;2) To quantify error growth due to model errors versus that due to uncertainties in analyses and boundary conditions;3) To assess the value of NAME observations for prediction;4) To help define field campaigns to follow NAME 2004.

Key Questions (ultimately critical for climate prediction):

1) How is the life cycle of the monsoon related to the evolution of oceanic and continental boundary conditions?

2) Can models reproduce the observed summertime precipitation in average years and years with strong SST influence?

ModelsOn board: NSIPP, NCEP/GFS; Possible: GFDL, NCAR

Jae-Kyung E. Schemm et al.

CPC/NCEP/NWS/NOAA

Different stages of modeling • Regional model simulations

Convection, diurnal cycle, rainfall distribution

regional features

• Observed SSTs– Global forecasts-> regional

Model nesting

• Two tier prediction system

Predicted SSTs – global model forecasts

• Coupled model prediction

NAME DELIVERABLES

• Observing system design for monitoring and predicting the North American monsoon system.

• More comprehensive understanding of North American summer climate variability and predictability.

• Strengthened multinational scientific collaboration across

Pan-America.

• Measurably improved climate models that predict North American monsoon variability months to seasons in advance.

NAME ROADMAP

Pre-NAME 2004 Activities:* Diagnostics and Analysis

- Model (e.g. NAMAP; Warm Season Diurnal Cycle in AGCM’s)- Reanalysis (global, regional)

* NAME FOC Practice Forecasting* Workshops

- NASA/CLIVAR Subseasonal Workshop / NAME Modeling Workshop- NAME SWG-5 / NAME Special Session (Puerto Vallarta)

NAME 2004 Activities:* NAME EOP Forecaster Support

- Forecast Discussions / Operational Assessments* Real-time Monitoring, Analysis and Forecast Products

NAME ROADMAPPost-NAME 2004 Activities

* Model and Forecast System Development- NAME CPT activities (simulation of convective precipitation) - Multi-scale modeling / CRM

* Experimental Prediction- NAME 2004 case studies / hindcasts- Sensitivity to SST and soil moisture (operational centers)- Subseasonal prediction (e.g. TISO.MJO)

* Diagnostics and Analysis- Reanalysis (global, regional, NAME data impact)- Model diagnostics (NAMAP 2)

* Applications and Product Development- Assessments (Hazards, North American drought monitor)- Forecasts (North American seasonal and subseasonal)- Applications (Agriculture, Fire WX, Water Resource)

* Research and Dataset Development- PACS-GAPP warm season precipitation initiative