scholarly journals On the representation of major stratospheric warmings in reanalyses

2019 ◽  
Author(s):  
Blanca Ayarzagüena ◽  
Froila M. Palmeiro ◽  
David Barriopedro ◽  
Natalia Calvo ◽  
Ulrike Langematz ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Climate Models ◽  
Good Representation ◽  
Future Projections ◽  
Triggering Mechanisms ◽  
Precursor Activity ◽  
Stratospheric Variability ◽  
Good Agreement

Abstract. Major sudden stratospheric warmings (SSWs) represent one of the most abrupt phenomena of the boreal wintertime stratospheric variability, and constitute the clearest example of coupling between the stratosphere and the troposphere. A good representation of SSWs in climate models is required to reduce their biases and uncertainties in future projections of stratospheric variability. The ability of models to reproduce these phenomena is usually assessed with just one reanalysis. However, the number of reanalyses has increased in the last decade and their own biases may affect the model evaluation. Here we compare the representation of the main aspects of SSWs across reanalyses. The examination of their main characteristics in the pre- and post-satellite periods reveals that reanalyses behave very similarly in both periods. However, discrepancies are larger in the pre-satellite period than afterwards, particularly for the NCEP/NCAR reanalysis. All datasets reproduce similarly the specific features of wavenumber-1 and wavenumber-2 SSWs. A good agreement among reanalyses is also found for triggering mechanisms, tropospheric precursors and surface fingerprint. In particular, differences in blocking precursor activity of SSWs across reanalyses are much smaller than between blocking definitions.

scholarly journals On the representation of major stratospheric warmings in reanalyses

2019 ◽  
Vol 19 (14) ◽  
pp. 9469-9484 ◽  
Author(s):  
Blanca Ayarzagüena ◽  
Froila M. Palmeiro ◽  
David Barriopedro ◽  
Natalia Calvo ◽  
Ulrike Langematz ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Climate Models ◽  
Good Representation ◽  
Future Projections ◽  
Surface Response ◽  
Triggering Mechanisms ◽  
Precursor Activity ◽  
Stratospheric Variability ◽  
Good Agreement

Abstract. Major sudden stratospheric warmings (SSWs) represent one of the most abrupt phenomena of the boreal wintertime stratospheric variability, and constitute the clearest example of coupling between the stratosphere and the troposphere. A good representation of SSWs in climate models is required to reduce their biases and uncertainties in future projections of stratospheric variability. The ability of models to reproduce these phenomena is usually assessed with just one reanalysis. However, the number of reanalyses has increased in the last decade and their own biases may affect the model evaluation. Here we compare the representation of the main aspects of SSWs across reanalyses. The examination of their main characteristics in the pre- and post-satellite periods reveals that reanalyses behave very similarly in both periods. However, discrepancies are larger in the pre-satellite period compared to afterwards, particularly for the NCEP-NCAR reanalysis. All datasets reproduce similarly the specific features of wavenumber-1 and wavenumber-2 SSWs. A good agreement among reanalyses is also found for triggering mechanisms, tropospheric precursors, and surface response. In particular, differences in blocking precursor activity of SSWs across reanalyses are much smaller than between blocking definitions.

The CORDEX‐Australasia ensemble: evaluation and future projections

2021 ◽  
Author(s):  
Jason Evans ◽  
Giovanni Di Virgilio ◽  
Annette Hirsch ◽  
Peter Hoffmann ◽  
Armelle Reca Remedio ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Current Status ◽  
Climate Projections ◽  
Good Representation ◽  
Future Projections ◽  
Recent Climate ◽  
Climate Downscaling

<p>The World Climate Research Programme (WCRP) has an international initiative called the COordinated Regional climate Downscaling EXperiment (CORDEX). The goal of the initiative is to provide regionally downscaled climate projections for most land regions of the globe, as a compliment to the global climate model projections performed within the Coupled Model Intercomparison Projects (CMIP). CORDEX includes data from both dynamical and statistical downscaling. It is anticipated that the CORDEX dataset will provide a link to the impacts and adaptation community through its better resolution and regional focus. Participation in CORDEX is open and any researchers performing climate downscaling are encourage to engage with the initiative. Here I present the current status, <span>evaluation and future projections</span> for the CORDEX-AustralAsia <span>ensemble</span>.</p><p>The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.</p>

scholarly journals Seasonal predictability of Mediterranean weather regimes in the Copernicus C3S systems

2021 ◽  
Author(s):  
Ignazio Giuntoli ◽  
Federico Fabiano ◽  
Susanna Corti
Keyword(s):  
Mediterranean Region ◽  
Climate Models ◽  
Reanalysis Data ◽  
Seasonal Predictability ◽  
Economic Implications ◽  
Weather Regimes ◽  
Weather Patterns ◽  
The Mediterranean ◽  
Good Agreement

AbstractSeasonal predictions in the Mediterranean region have relevant socio-economic implications, especially in the context of a changing climate. To date, sources of predictability have not been sufficiently investigated at the seasonal scale in this region. To fill this gap, we explore sources of predictability using a weather regimes (WRs) framework. The role of WRs in influencing regional weather patterns in the climate state has generated interest in assessing the ability of climate models to reproduce them. We identify four Mediterranean WRs for the winter (DJF) season and explore their sources of predictability looking at teleconnections with sea surface temperature (SST). In particular, we assess how SST anomalies affect the WRs frequencies during winter focussing on the two WRs that are associated with the teleconnections in which the signal is more intense: the Meridional and the Anticyclonic regimes. These sources of predictability are sought in five state-of-the-art seasonal forecasting systems included in the Copernicus Climate Change Services (C3S) suite finding a weaker signal but an overall good agreement with reanalysis data. Finally, we assess the ability of the C3S models in reproducing the reanalysis data WRs frequencies finding that their moderate skill increases during ENSO intense years, indicating that this teleconnection is well reproduced by the models and yields improved predictability in the Mediterranean region.

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1339-1357 ◽  
Author(s):  
Peter B. Gibson ◽  
Duane E. Waliser ◽  
Huikyo Lee ◽  
Baijun Tian ◽  
Elias Massoud
Keyword(s):  
United States ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Precipitation Indices

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals Dry Spells and Extreme Precipitation are The Main Trigger of Landslides in Central Europe

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Radek Tichavský ◽  
Juan Antonio Ballesteros-Cánovas ◽  
Karel Šilhán ◽  
Radim Tolasz ◽  
Markus Stoffel
Keyword(s):  
Time Series ◽  
Central Europe ◽  
Climate Models ◽  
Regional Scale ◽  
Summer Precipitation ◽  
Greenhouse Climate ◽  
Dry Spells ◽  
Outer Western Carpathians ◽  
Triggering Mechanisms

Abstract Landslides are frequently triggered by extreme meteorological events which has led to concern and debate about their activity in a future greenhouse climate. It is also hypothesized that dry spells preceding triggering rainfall may increase slope predisposition to sliding, especially in the case of clay-rich soils. Here we combined dendrogeomorphic time series of landslides and climatic records to test the possible role of dry spells and extreme downpours on process activity in the Outer Western Carpathians (Central Europe). To this end, we tested time series of past frequencies and return periods of landslide reactivations at the regional scale with a Generalized Linear Mixed (GLM) model to explore linkages between landslide occurrences and triggering climate variables. Results show that landslide reactivations are concentrated during years in which spring and summer precipitation sums were significantly higher than usual, and that triggering mechanisms vary between different types of landslides (i.e. complex, shallow or flow-like). The GLM model also points to the susceptibility of landslide bodies to the combined occurrence of long, dry spells followed by large precipitation. Such situations are likely to increase in frequency in the future as climate models predict an enhancement of heatwaves and dry spells in future summers, that would be interrupted by less frequent, yet more intense storms, especially also in mountain regions.

scholarly journals On the Gravity Wave Forcing during the Southern Stratospheric Final Warming in LMDZ

2016 ◽  
Vol 73 (8) ◽  
pp. 3213-3226 ◽  
Author(s):  
Alvaro de la Cámara ◽  
François Lott ◽  
Valérian Jewtoukoff ◽  
Riwal Plougonven ◽  
Albert Hertzog
Keyword(s):  
Gravity Wave ◽  
General Circulation ◽  
Climate Models ◽  
Middle Atmosphere ◽  
Circulation Model ◽  
High Amplitude ◽  
Austral Spring ◽  
Wave Forcing ◽  
Stratospheric Final Warming ◽  
Good Agreement

Abstract The austral stratospheric final warming date is often predicted with substantial delay in several climate models. This systematic error is generally attributed to insufficient parameterized gravity wave (GW) drag in the stratosphere around 60°S. A simulation with a general circulation model [Laboratoire de Météorologie Dynamique zoom model (LMDZ)] with a much less pronounced bias is used to analyze the contribution of the different types of waves to the dynamics of the final warming. For this purpose, the resolved and unresolved wave forcing of the middle atmosphere during the austral spring are examined in LMDZ and reanalysis data, and a good agreement is found between the two datasets. The role of parameterized orographic and nonorographic GWs in LMDZ is further examined, and it is found that orographic and nonorographic GWs contribute evenly to the GW forcing in the stratosphere, unlike in other climate models, where orographic GWs are the main contributor. This result is shown to be in good agreement with GW-resolving operational analysis products. It is demonstrated that the significant contribution of the nonorographic GWs is due to highly intermittent momentum fluxes produced by the source-related parameterizations used in LMDZ, in qualitative agreement with recent observations. This yields sporadic high-amplitude GWs that break in the stratosphere and force the circulation at lower altitudes than more homogeneously distributed nonorographic GW parameterizations do.

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