scholarly journals Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

2022 ◽  
Keyword(s):  
Long Range ◽  
Gaussian Processes ◽  
Climate Model ◽  
Model Performance ◽  
Skill Score ◽  
Near Surface ◽  
Mean Temperature ◽  
Air Temperatures ◽  
Improve Model ◽  
Heidke Skill Score

Abstract We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

scholarly journals Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble

2014 ◽  
Vol 7 (1) ◽  
pp. 217-293 ◽  
Author(s):  
S. Kotlarski ◽  
K. Keuler ◽  
O. B. Christensen ◽  
A. Colette ◽  
M. Déqué ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Quality Standard ◽  
European Continent ◽  
Near Surface ◽  
Mean Values ◽  
European Climate

Abstract. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatio-temporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS dataset as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight sub-domains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies non-negligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and south-eastern Europe reflect common model biases. For seasonal mean quantities averaged over large European sub-domains, no clear benefit of an increased spatial resolution (12 km vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

scholarly journals Evaluation of the Sea-Ice Simulation in the Upgraded Version of the Coupled Regional Atmosphere-Ocean- Sea Ice Model HIRHAM–NAOSIM 2.0

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 431 ◽  
Author(s):  
Wolfgang Dorn ◽  
Annette Rinke ◽  
Cornelia Köberle ◽  
Klaus Dethloff ◽  
Rüdiger Gerdes
Keyword(s):  
Sea Ice ◽  
Current Model ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Near Surface ◽  
Previous Version ◽  
Air Temperatures ◽  
Ice Model

The sea-ice climatology and sea-ice trends and variability are evaluated in simulations with the new version of the coupled Arctic atmosphere-ocean-sea ice model HIRHAM–NAOSIM 2.0. This version utilizes upgraded model components for the coupled subsystems, which include physical and numerical improvements and higher horizontal and vertical resolution, and a revised coupling procedure with the aid of the coupling software YAC (Yet Another Coupler). The model performance is evaluated against observationally based data sets and compared with the previous version. Ensemble simulations for the period 1979–2016 reveal that Arctic sea ice is thicker in all seasons and closer to observations than in the previous version. Wintertime biases in sea-ice extent, upper ocean temperatures, and near-surface air temperatures are reduced, while summertime biases are of similar magnitude as in the previous version. Problematic issues of the current model configuration and potential corrective measures and further developments are discussed.

scholarly journals Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble

2014 ◽  
Vol 7 (4) ◽  
pp. 1297-1333 ◽  
Author(s):  
S. Kotlarski ◽  
K. Keuler ◽  
O. B. Christensen ◽  
A. Colette ◽  
M. Déqué ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
Model Performance ◽  
Quality Standard ◽  
Data Set ◽  
European Continent ◽  
Near Surface ◽  
Mean Values ◽  
European Climate

Abstract. EURO-CORDEX is an international climate downscaling initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989–2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The ensemble consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven ENSEMBLES simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX ensemble mostly correspond to those of the ENSEMBLES simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.

scholarly journals Permafrost Thaw and Ground Settlement Considering Long-Term Climate Impact in Northern Alaska

2021 ◽  
Author(s):  
Joey Yang ◽  
Kannon C. Lee ◽  
Haibo Liu
Keyword(s):  
Active Layer ◽  
Air Temperature ◽  
Climate Model ◽  
Ground Surface ◽  
North Slope ◽  
Active Layer Thickness ◽  
Near Surface ◽  
Surface Conditions ◽  
Air Temperatures ◽  
Ice Content

Abstract Alaska’s North Slope is predicted to experience twice the warming expected globally. When summers are longer and winters are shortened, ground surface conditions in the Arctic are expected to change considerably. This is significant for Arctic Alaska, a region that supports surface infrastructure such as energy extraction and transport assets (pipelines), buildings, roadways, and bridges. Climatic change at the ground surface has been shown to infiltrate soil layers beneath through the harmonic fluctuation of the active layer. Past studies found that warmer air temperature resulted in increasingly deeper thaw, leading to a deeper active layer. This study attempts to assess climate change based on the climate model data from the fifth phase of the Coupled Model Intercomparison Project and its impact on a study site on the North Slope. The predicted air temperature data are analyzed to evaluate how the freezing and thawing indices will change due to climate warming. A thermal model was developed that incorporated a ground surface condition defined by either undisturbed intact tundra or a gravel fill surface and applied climate model predicted air temperatures. Results indicate similar fluctuation in active layer thickness and values that fall within the range of minimum and maximum readings. It is found that the active layer thickens when the ground surface is either gravel fill or undisturbed tundra, but its thickness varies based on climate model predictions. These variations in active layer thickness are then analyzed by considering the near-surface frozen soil ice content. Analysis of results indicates that strain is most significant in the near-surface layers during thaw, indicating that settlement would be concurrent with annual thaw penetration. From this study, the climate model predicted air temperatures for a warming Arctic suggest that the thaw of near-surface frozen ground is largely dependent on ground surface conditions and the thermal properties of soil. Moreover, ice content is a major factor in the settlement predictions on Alaska’s North Slope. This study's results can help enhance the resilience of the existing and future new infrastructure in a changing Arctic environment.

The roles of winds and waves in Arctic sea ice variability

2021 ◽  
Author(s):  
Lettie Roach ◽  
Edward Blanchard-Wrigglesworth ◽  
Cecilia Bitz
Keyword(s):  
Sea Ice ◽  
Prediction Models ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Coupled Modelling ◽  
Ocean Surface Waves ◽  
Air Temperatures ◽  
Arctic Sea

<p><span>It is broadly accepted that variability and trends in Arctic sea ice remain poorly simulated even in the most state-of-the-art coupled climate and climate prediction models. Here, we show that a modern coupled climate model (CESM1) is in fact able to reproduce the observed variability and decline in summer sea ice when winds are nudged towards values from reanalysis.<span>  </span>We argue that the nudged-winds framework provides a straightforward way of evaluating models by removing much of the contribution of internal variability, revealing model successes and biases. The results demonstrate the importance of atmospheric circulation in driving interannual variability in sea ice and near-surface air temperatures, particularly in the summer. Finally, we will discuss the potential role of ocean surface waves in driving variability in Arctic sea ice, based on observational analysis and new coupled modelling results.</span></p>

scholarly journals HIRHAM–NAOSIM 2.0: The upgraded version of the coupled regional atmosphere-ocean-sea ice model for Arctic climate studies

2018 ◽  
Author(s):  
Wolfgang Dorn ◽  
Annette Rinke ◽  
Cornelia Köberle ◽  
Klaus Dethloff ◽  
Rüdiger Gerdes
Keyword(s):  
Sea Ice ◽  
Current Model ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Near Surface ◽  
Previous Version ◽  
Air Temperatures ◽  
Ice Model

Abstract. A new version of the coupled Arctic atmosphere-ocean-sea ice model HIRHAM-NAOSIM is described. This version utilizes upgraded model components for the coupled subsystems, which include physical and numerical improvements and higher horizontal and vertical resolution, and a revised coupling procedure with the aid of the coupling software YAC. The model performance is evaluated against observationally based data sets and compared with the previous version. Ensemble simulations for the period 1979–2016 reveal that Arctic sea ice is thicker in all seasons and closer to observations than in the previous version. Wintertime biases in sea-ice extent and near-surface air temperatures are reduced, while summertime biases are of similar magnitude as in the previous version. Problematic issues of the current model configuration and potential corrective measures and further developments are discussed.

scholarly journals Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

2019 ◽  
Vol 12 (12) ◽  
pp. 5229-5249 ◽  
Author(s):  
Emmanuele Russo ◽  
Ingo Kirchner ◽  
Stephan Pfahl ◽  
Martijn Schaap ◽  
Ulrich Cubasch
Keyword(s):  
Regional Climate Model ◽  
Central Asia ◽  
Climate Model ◽  
Reference Model ◽  
Developing Economies ◽  
Regional Climate ◽  
Model Performance ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Near Surface

Abstract. Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role. In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is two-fold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration. Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of climatological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability. This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

scholarly journals Climate Impacts from Afforestation and Deforestation in Europe

2019 ◽  
Vol 23 (1) ◽  
pp. 1-27 ◽  
Author(s):  
G. Strandberg ◽  
E. Kjellström
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Climate Model ◽  
Climate Impacts ◽  
Heat Fluxes ◽  
Maximum Temperature ◽  
Vegetation Changes ◽  
Near Surface ◽  
Local Climate ◽  
Mean Temperature

Abstract Changes in vegetation are known to have an impact on climate via biogeophysical effects such as changes in albedo and heat fluxes. Here, the effects of maximum afforestation and deforestation are studied over Europe. This is done by comparing three regional climate model simulations—one with present-day vegetation, one with maximum afforestation, and one with maximum deforestation. In general, afforestation leads to more evapotranspiration (ET), which leads to decreased near-surface temperature, whereas deforestation leads to less ET, which leads to increased temperature. There are exceptions, mainly in regions with little water available for ET. In such regions, changes in albedo are relatively more important for temperature. The simulated biogeophysical effect on seasonal mean temperature varies between 0.5° and 3°C across Europe. The effect on minimum and maximum temperature is larger than that on mean temperature. Increased (decreased) mean temperature is associated with an even larger increase (decrease) in maximum summer (minimum winter) temperature. The effect on precipitation is found to be small. Two additional simulations in which vegetation is changed in only one-half of the domain were also performed. These simulations show that the climatic effects from changed vegetation in Europe are local. The results imply that vegetation changes have had, and will have, a significant impact on local climate in Europe; the climatic response is comparable to climate change under RCP2.6. Therefore, effects from vegetation change should be taken into account when simulating past, present, and future climate for this region. The results also imply that vegetation changes could be used to mitigate local climate change.

scholarly journals A model-based assessment of the effects of projected climate change on the water resources of Jordan

2010 ◽  
Vol 368 (1931) ◽  
pp. 5151-5172 ◽  
Author(s):  
A. J. Wade ◽  
E. Black ◽  
D. J. Brayshaw ◽  
M. El-Bastawesy ◽  
P. A. C. Holmes ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Model ◽  
Regional Climate ◽  
Water Security ◽  
Annual Rainfall ◽  
Near Surface ◽  
Modelling Framework ◽  
Air Temperatures ◽  
Extreme Flood

This paper is concerned with the quantification of the likely effect of anthropogenic climate change on the water resources of Jordan by the end of the twenty-first century. Specifically, a suite of hydrological models are used in conjunction with modelled outcomes from a regional climate model, HadRM3, and a weather generator to determine how future flows in the upper River Jordan and in the Wadi Faynan may change. The results indicate that groundwater will play an important role in the water security of the country as irrigation demands increase. Given future projections of reduced winter rainfall and increased near-surface air temperatures, the already low groundwater recharge will decrease further. Interestingly, the modelled discharge at the Wadi Faynan indicates that extreme flood flows will increase in magnitude, despite a decrease in the mean annual rainfall. Simulations projected no increase in flood magnitude in the upper River Jordan. Discussion focuses on the utility of the modelling framework, the problems of making quantitative forecasts and the implications of reduced water availability in Jordan.

scholarly journals Developments in Operational Long-Range Climate Prediction at CPC

2008 ◽  
Vol 23 (3) ◽  
pp. 496-515 ◽  
Author(s):  
Edward A. O’Lenic ◽  
David A. Unger ◽  
Michael S. Halpert ◽  
Kenneth S. Pelman
Keyword(s):  
Long Range ◽  
Southern Oscillation ◽  
Skill Score ◽  
Climate Prediction ◽  
Direct Access ◽  
Production Methods ◽  
Heidke Skill Score ◽  
User Access ◽  
Environmental Prediction ◽  
Climate Prediction Center

Abstract The science, production methods, and format of long-range forecasts (LRFs) at the Climate Prediction Center (CPC), a part of the National Weather Service’s (NWS’s) National Centers for Environmental Prediction (NCEP), have evolved greatly since the inception of 1-month mean forecasts in 1946 and 3-month mean forecasts in 1982. Early forecasts used a subjective blending of persistence and linear regression-based forecast tools, and a categorical map format. The current forecast system uses an increasingly objective technique to combine a variety of statistical and dynamical models, which incorporate the impacts of El Niño–Southern Oscillation (ENSO) and other sources of interannual variability, and trend. CPC’s operational LRFs are produced each midmonth with a “lead” (i.e., amount of time between the release of a forecast and the start of the valid period) of ½ month for the 1-month outlook, and with leads ranging from ½ month through 12½ months for the 3-month outlook. The 1-month outlook is also updated at the end of each month with a lead of zero. Graphical renderings of the forecasts made available to users range from a simple display of the probability of the most likely tercile to a detailed portrayal of the entire probability distribution. Efforts are under way at CPC to objectively weight, bias correct, and combine the information from many different LRF prediction tools into a single tool, called the consolidation (CON). CON ½-month lead 3-month temperature (precipitation) hindcasts over 1995–2005 were 18% (195%) better, as measured by the Heidke skill score for nonequal chances forecasts, than real-time official (OFF) forecasts during that period. CON was implemented into LRF operations in 2006, and promises to transfer these improvements to the official LRF. Improvements in the science and production methods of LRFs are increasingly being driven by users, who are finding an increasing number of applications, and demanding improved access to forecast information. From the forecast-producer side, hope for improvement in this area lies in greater dialogue with users, and development of products emphasizing user access, input, and feedback, including direct access to 5 km × 5 km gridded outlook data through NWS’s new National Digital Forecast Database (NDFD).

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