Valérie Dulière

Evaluation of WRF and HadRM Mesoscale Climate Simulations over the U.S. Pacific Northwest

Abstract This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role ...

Extreme Precipitation and Temperature over the U.S. Pacific Northwest: A Comparison between Observations, Reanalysis Data, and Regional Models*

AbstractExtreme precipitation and temperature indices in reanalysis data and regional climate models are compared to station observations. The regional models represent most indices of extreme temperature well. For extreme precipitation, finer grid spacing considerably improves the match to observations. Three regional models, the Weather Research and Forecasting (WRF) at 12- and 36-km grid spacing and the Hadley Centre Regional Model (HadRM) at 25-km grid spacing, are forced with global reanalysis fields over the U.S. Pacific Northwest during 2003–07. The reanalysis data represent the timing of rain-bearing storms over the Pacific Northwest well; however, the reanalysis has the worst performance at simulating both extreme precipitation indices and extreme temperature indices when compared to the WRF and HadRM simulations. These results suggest that the reanalysis data and, by extension, global climate model simulations are not sufficient for examining local extreme precipitations and temperatures owing t...

ENSO anomalies over the Western United States: present and future patterns in regional climate simulations

Surface temperature, precipitation, specific humidity and wind anomalies associated with the warm and cold phases of ENSO simulated by WRF and HadRM are examined for the present and future decades. WRF is driven by ECHAM5 and CCSM3, respectively, and HadRM is driven by HadCM3. For the current decades, all simulations show some capability in resolving the observed warm-dry and cool-wet teleconnection patterns over the PNW and the Southwest U.S. for warm and cold ENSO. Differences in the regional simulations originate primarily from the respective driving fields. For the future decades, the warm-dry and cool-wet teleconnection patterns in association with ENSO are still represented in ECHAM5-WRF and HadRM. However, there are indications of changes in the ENSO teleconnection patterns for CCSM3-WRF in the future, with wet anomalies dominating in the PNW and the Southwest U.S. for both warm and cold ENSO, in contrast to the canonical patterns of precipitation anomalies. Interaction of anomalous wind flow with local terrain plays a critical role in the generation of anomalous precipitation over the western U.S. Anomalous dry conditions are always associated with anomalous airflow that runs parallel to local mountains and wet conditions with airflow that runs perpendicular to local mountains. Future changes in temperature and precipitation associated with the ENSO events in the regional simulations indicate varying responses depending on the variables examined as well as depending on the phase of ENSO.

Changes in Twentieth-Century Extreme Temperature and Precipitation over the Western United States Based on Observations and Regional Climate Model Simulations*

Trends in extreme temperature and precipitation in two regional climate model simulations forced by two global climate models are compared with observed trends over the western United States. The observed temperature extremes show substantial and statistically significant trends across the western United States during the late twentieth century, with consistent results among individual stations. The two regional climate models simulate temporal trends that are consistent with the observed trends and reflect the anthropogenic warmingsignal.Incontrast,nosuchcleartrends orcorrespondencebetweentheobservations andsimulationsis found for extreme precipitation, likely resulting from the dominance of the natural variability over systematic climate change during the period. However, further analysis of the variability of precipitation extremes shows strong correspondence between the observed precipitation indices and increasing oceanic Ni~ index (ONI), with regionally coherent patterns found for the U.S. Northwest and Southwest. Both regional climate simulations reproduce the observed relationship with ONI, indicating that the models can represent the large-scale climaticlinkswithextremeprecipitation.TheregionalclimatemodelsimulationsusetheWeatherResearchand

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.