Arctic System Model Workshop Boulder, Colorado

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

David H. Bromwich1 Ola Persson2,and John J. Cassano3

1Polar Meteorology GroupByrd Polar Research Center

The Ohio State UniversityColumbus, Ohio, USA

2National Oceanic and Atmospheric Administration Earth System Research Laboratory

Cooperative Institute for Research in Environmental Sciences3University of Colorado

Cooperative Institute for Research in Environmental SciencesDepartment of Atmospheric and Oceanic Sciences

Atmosphere Observational Needs for Model Validation

Atmosphere Observational Needs for Model Validation

Just What Is “Arctic”?

• Arctic Ocean

• Greenland Ice Sheet …and …

• The rivers which empty into the Arctic Ocean and maintain its near surface stratification

We Select

Thus our Arctic extends from ~45°N to the North Pole

Arctic System Model Workshop Boulder, Colorado

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

Selected Weather and Climate Features and their Resolvability with Current Arctic ObservationsSelected Weather and Climate Features and their Resolvability with Current Arctic Observations

Climate Modes– e.g., Arctic Oscillation/NAO, PNA– Yes, we can resolve these large-scale features with current observations

Weather– e.g., Synoptic-scale cyclones– Reasonably, but some shortcomings – see next slide

Mesoscale Phenomena– Polar lows, sea breezes, barrier winds, katabatic winds– Insufficiently resolved

Sea Ice– Extent, fractional coverage, thickness, albedo, snow cover, melt ponds– Yes Yes No ? ? ??

Land – Snow cover, SWE, permafrost, vegetation, lakes– Yes ? ? Yes ?

The storm of 19 October 2004 as depicted by the NCEP/NCAR global reanalysis. Contours represent isobars of sea level pressure at increments of 3 hPa. [from visualization package of NOAA Climate Diagnostics Center]

The Figure shows an intense storm depicted in the NCEP/NCAR reanalysis for 19 October 2004. This storm, which led to flooding of downtown Nome, Alaska, has a central pressure of 949 hPa in the reanalysis. The actual central pressure deduced by the National Weather Service was as low as 941 hPa.

3 Critical Components3 Critical Components for an for an Integrated Arctic Observing Integrated Arctic Observing SystemSystem• Remote Sensing ObservationsRemote Sensing Observations

– only way to obtain comprehensive only way to obtain comprehensive regional coverageregional coverage

• Numerical ModelingNumerical Modeling– Fills gaps in the systemFills gaps in the system– Maintains physical consistency in the Maintains physical consistency in the

systemsystem

• In-situ ObservationsIn-situ Observations– Provides the ground truth to calibrate the Provides the ground truth to calibrate the

systemsystem

Typical distribution of COSMIC GPS radio occultation soundings (green dots) over a 24-hour period over the Arctic.

Greenland Climate Network (GC-Net)

HUMTUN

GITNGP

NAE

NAU

SUM

KARCP1CP2

JAR1,2,3,SWC

NSE

SDL

SDM

DY2

Steffen and Box (2001), JGR

Arctic System Model Workshop Boulder, Colorado

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, OhioIASOA ObservatoriesIASOA Observatories

Data of interest to the IASOA consortium include measurements of standard meteorology, greenhouse gases, atmospheric radiation, clouds, pollutants, chemistry, aerosols, and surface energy balances. 

Arctic System Model Workshop Boulder, Colorado

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

Tara Arctic 2007-2008 is a specific project for IPY. The boat will undertake a Nansen-like crossing of the Arctic Ocean, drifting from the north of Siberia to the Fram Straight during almost two year trapped in the ice. The Tara Arctic ice station will provide permanent facilities for science fieldwork, in-situ observations and maintenance possibilities of probes and automated buoys.

Tara Ice Station

Arctic System Reanalysis Arctic System Reanalysis (ASR)(ASR)an NSF-Funded IPY Projectan NSF-Funded IPY Project1. Rapid climate change appears to be happening in the Arctic.

A more comprehensive picture of the coupled atmosphere/land surface/ ocean interactions is needed.

2. Global reanalyses encounter many problems at high latitudes. The ASR would use the best available description for Arctic processes and would enhance the existing database of Arctic observations. The ASR will be produced at improved temporal resolution and much higher spatial resolution.

3. The ASR would provide fields for which direct observation are sparse or problematic (precipitation, radiation, cloud, ...) at higher resolution than from existing reanalyses.

4. The system-oriented approach would provide a community focus including the atmosphere, land surface and sea ice communities.

5. The ASR would provide a convenient synthesis of Arctic field programs (SHEBA, LAII/ATLAS, ARM, ...)

Optimizing the Arctic Observing Network Using the ASR

Framework

• Observing System Experiments (OSEs) are numerical model-based experiments to test the impact of existing observations. Sometimes called “data denial” experiments.

• Observing System Simulation Experiments (OSSEs) are numerical experiments that test impacts of future observing systems, e.g., new satellite sensors and AWS.

• Determine the observations needed to optimize the observing system.

Key Points for SAONKey Points for SAON

New Data Sources Weather and Climate Applications Combining Remote Sensing, Modeling and In-situ

Observations Data Assimilation Bringing Observations, Modeling and Data Users

together Arctic System Reanalysis as a Synthesis Better Integrated Use of Resources User Friendly Data Handling

Atmospheric Model EvaluationAtmospheric Model Evaluation

Evaluate over a variety of polar surface types– Ice sheet – Sea ice / ocean – Non-ice covered land

Evaluate atmospheric state– Temperature, pressure, winds, humidity,…

Evaluate atmospheric processes and relationships– Surface energy budget– Cloud processes– Are we getting the right answer for the right reasons?– Do the relationships occurring in the data also occur in the

models?

Comparison to ERA40

Comparison with SHEBA surface observations

Example: ARCMIP EvaluationExample: ARCMIP Evaluation

• Need to evaluate models on several scales

- At largest scales need to compare model to reanalyses

- At smaller scales can compare model to point observations, although care is needed

Comparison to SHEBA sensible heat flux

Comparison to SHEBA latent heat flux

- Large accumulated errors in almost all models are of concern in coupled simulations

ARCMIP comparisons of sensible heat flux relationship

- Only one model is able to decrease the magnitude of the Hs for very stable conditions as in the observations

Analysis of relationship between variables: SWD and CWP

It is important to not only evaluate the model state but to evaluate if the model reproduces observed relationships between variables

ConclusionsConclusions Care needs to be taken when evaluating variables

that are the result of many interacting, complex processes– It is useful to evaluate the different processes that are

responsible for the final state

Evaluation of processes and relationships between variables can provide additional insight into model performance– Useful to highlight aspects of the model that need

improvement