scholarly journals The KLIWAS North Sea Climatology. Part I: Processing of the Atmospheric Data

2018 ◽  
Vol 35 (1) ◽  
pp. 111-126 ◽  
Author(s):  
Remon Sadikni ◽  
Nils H. Schade ◽  
Axel Andersson ◽  
Annika Jahnke-Bornemann ◽  
Iris Hinrichs ◽  
...  
Keyword(s):  
North Sea ◽  
Climate Models ◽  
Sampling Error ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Data ◽  
The North ◽  
Dewpoint Temperature ◽  
The Baltic ◽  
The University

AbstractClimatological reference data serve as validation of regional climate models, as the boundary condition for the model runs, and as input for assimilation systems used by reanalyses. Within the framework of the interdisciplinary research program Climate Water Navigation (KLIWAS): Impacts of Climate Change on Waterways and Navigation of the German Federal Ministry of Transport and Digital Infrastructure, a new climatology of the North Sea and adjacent regions was developed in an joint effort by the Federal Maritime and Hydrographic Agency, the German Weather Service [Deutscher Wetterdienst (DWD)], and the Integrated Climate Data Center (ICDC) of the University of Hamburg. Long-term records of monthly and annual mean 2-m air temperature, dewpoint temperature, and sea level pressure data from 1950 to 2010 were calculated on a horizontal 1° × 1° grid. All products were based on quality-controlled data from DWD’s Marine Data Centre. Correction methods were implemented for each parameter to reduce the sampling error resulting from the sparse coverage of observations in certain regions. Comparisons between sampling error estimates based on ERA-40 and the climatology products show that the sampling error was reduced effectively. The climatologies are available for download on the ICDC’s website and will be updated regularly regarding new observations and additional parameters. An extension to the Baltic Sea is in progress.

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Data ◽  
Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

scholarly journals Spatial and temporal characteristics in streamflow-related hydroclimatic variables over western Canada. Part 2: future projections

2016 ◽  
Vol 48 (4) ◽  
pp. 932-944 ◽  
Author(s):  
H. C. L. O'Neil ◽  
T. D. Prowse ◽  
B. R. Bonsal ◽  
Y. B. Dibike
Keyword(s):  
Snow Depth ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cold Season ◽  
Maximum Temperature ◽  
Western Canada ◽  
The North ◽  
Spring Freshet ◽  
Hydroclimatic Variables

Much of the freshwater in western Canada originates in the Rocky Mountains as snowpack. Temperature and precipitation patterns throughout the region control the amount of snow accumulated and stored throughout the winter, and the intensity and timing of melt during the spring freshet. Therefore, changes in temperature, precipitation, snow depth, and snowmelt over western Canada are examined through comparison of output from the current and future periods of a series of regional climate models for the time periods 1971–2000 and 2041–2070. Temporal and spatial analyses of these hydroclimatic variables indicate that minimum temperature is likely to increase more than maximum temperature, particularly during the cold season, possibly contributing to earlier spring melt. Precipitation is projected to increase, particularly in the north. In the coldest months of the year snow depth is expected to increase in northern areas and decrease across the rest of study area. Snowmelt results indicate increases in mid-winter melt events and an earlier onset of the spring freshet. This study provides a summary of potential future climate using key hydroclimatic variables across western Canada with regard to the effects these changes may have on streamflow and the spring freshet, and thus water resources, throughout the study area.

scholarly journals Species Favourability Shift in Europe due to Climate Change: A Case Study for Fagus sylvatica L. and Picea abies (L.) Karst. Based on an Ensemble of Climate Models

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Wolfgang Falk ◽  
Nils Hempelmann
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Climate Models ◽  
Species Distribution Model ◽  
Regional Climate ◽  
European Beech ◽  
Regional Climate Models ◽  
Distribution Model ◽  
Continental Scale ◽  
The North

Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.

scholarly journals Climate indices for Baltic States from principal component analysis

2017 ◽  
Author(s):  
Liga Bethere ◽  
Juris Sennikovs ◽  
Uldis Bethers
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Climate Models ◽  
Regional Climate ◽  
Principal Component ◽  
Regional Climate Models ◽  
Component Analysis ◽  
Climate Indices ◽  
Baltic States ◽  
The Baltic

Abstract. We used principal component analysis (PCA) to derive climate indices that describe the main spatial features of the climate in the Baltic States (Estonia, Latvia and Lithuania). Monthly mean temperature and total precipitation values derived from the ensemble of bias-corrected regional climate models (RCM) were used. Principal components were derived for years 1961–1990. The first three components describe 92 % of the variance of the initial data and were chosen as climate indices in further analysis. Spatial patterns of these indices and their correlation with the initial variables were analyzed and it was observed that higher values of each index corresponded to: (1) less distinct seasonality, (2) warmer and (3) wetter climate. The loadings from the chosen principal components were then further used to calculate values of the climate indices for years 2071–2100. Overall increase was found for all three indices with minimal changes in their spatial pattern.

scholarly journals Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

2021 ◽  
Vol 22 (4) ◽  
pp. 407-418
Author(s):  
SHWETA PANJWANI ◽  
S. NARESH KUMAR ◽  
LAXMI AHUJA
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Regional Climate ◽  
India Meteorological Department ◽  
Regional Climate Models ◽  
Climate Data ◽  
Simulation Performance ◽  
Climate Change Assessment ◽  
Better Than

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.

scholarly journals Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

2021 ◽  
Vol 14 (3) ◽  
pp. 1267-1293
Author(s):  
Sara Top ◽  
Lola Kotova ◽  
Lesley De Cruz ◽  
Svetlana Aniskevich ◽  
Leonid Bobylev ◽  
...  
Keyword(s):  
High Resolution ◽  
Central Asia ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
The Tibetan Plateau ◽  
Climate Information ◽  
Climate Data

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

scholarly journals A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yun Xu ◽  
Andrew Jones ◽  
Alan Rhoades
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Driving Forces ◽  
Regional Climate ◽  
Model Development ◽  
Regional Climate Models ◽  
The North ◽  
Snow Water ◽  
Regional Downscaling

Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

scholarly journals Quantile Mapping Bias Correction on Rossby Centre Regional Climate Models for Precipitation Analysis over Kenya, East Africa

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 801 ◽  
Author(s):  
Brian Ayugi ◽  
Guirong Tan ◽  
Niu Ruoyun ◽  
Hassen Babaousmail ◽  
Moses Ojara ◽  
...  
Keyword(s):  
Bias Correction ◽  
Impact Analysis ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Absolute Error ◽  
Intermediate Step ◽  
Climate Data ◽  
Quantile Mapping ◽  
Mapping Bias

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March–May (MAM) and October–December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

scholarly journals Development of Climate Data Bias Corrector (CDBC) Tool and Its Application over the Agro-Ecological Zones of India

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1102 ◽  
Author(s):  
Rishabh Gupta ◽  
Rabin Bhattarai ◽  
Ashok Mishra
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Computational Cost ◽  
Regional Climate Models ◽  
Climate Data ◽  
Ecological Zones ◽  
Station Grid ◽  
File Formats ◽  
Agro Ecological Zones

The use of global and regional climate models has been increasing in the past few decades, in order to analyze the future of natural resources and the socio-economic aspects of climate change. However, these climate model outputs can be quite biased, which makes it challenging to use them directly for analysis purpose. Therefore, a tool named Climate Data Bias Corrector was developed to correct the bias in climatic projections of historical and future periods for three primary climatic variables—rainfall, temperature (maximum and minimum), and solar radiation. It uses the quantile mapping approach, known for its efficiency and low computational cost for bias correction. Its Graphical User Interface (GUI) was made to be feasible to take input and give output in commonly used file formats—comma and tab delimited file formats. It also generates month-wise cumulative density function (CDF) plot of a random station/grid to allow the user to investigate the effectiveness of correction statistically. The tool was verified with a case study on several agro-ecological zones of India and found to be efficient.

scholarly journals Future Decreases in Freezing Days across North America

2016 ◽  
Vol 29 (19) ◽  
pp. 6923-6935 ◽  
Author(s):  
Michael A. Rawlins ◽  
Raymond S. Bradley ◽  
Henry F. Diaz ◽  
John S. Kimball ◽  
David A. Robinson
Keyword(s):  
North America ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
The United States ◽  
Rate Of Change ◽  
Mean Annual Temperature ◽  
The North ◽  
Air Temperatures ◽  
Climate Change Assessment

Abstract This study used air temperatures from a suite of regional climate models participating in the North American Climate Change Assessment Program (NARCCAP) together with two atmospheric reanalysis datasets to investigate changes in freezing days (defined as days with daily average temperature below freezing) likely to occur between 30-yr baseline (1971–2000) and midcentury (2041–70) periods across most of North America. Changes in NARCCAP ensemble mean winter temperature show a strong gradient with latitude, with warming of over 4°C near Hudson Bay. The decline in freezing days ranges from less than 10 days across north-central Canada to nearly 90 days in the warmest areas of the continent that currently undergo seasonally freezing conditions. The area experiencing freezing days contracts by 0.9–1.0 × 106 km2 (5.7%–6.4% of the total area). Areas with mean annual temperature between 2° and 6°C and a relatively low rate of change in climatological daily temperatures (<0.2°C day−) near the time of spring thaw will encounter the greatest decreases in freezing days. Advances in the timing of spring thaw will exceed the delay in fall freeze across much of the United States, with the reverse pattern likely over most of Canada.

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