The DWD climate prediction website

2021 ◽  
Author(s):  
Andreas Paxian ◽  
Katja Reinhardt ◽  
Birgit Mannig ◽  
Katharina Isensee ◽  
Amelie Krug ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Prediction ◽  
Climate Projections ◽  
Climate Data ◽  
Traffic Light ◽  
Daily Work ◽  
Bias Drift ◽  
Climate Service ◽  
German Cities ◽  
Decadal Predictions

<p>DWD provides operational seasonal and decadal predictions of the German climate prediction system since 2016 and 2020, respectively. We plan to present these predictions together with post-processed ECMWF sub-seasonal forecast products on the DWD climate prediction website www.dwd.de/climatepredictions. In March 2020, this climate service was published with decadal predictions for the coming years; sub-seasonal and seasonal predictions for the coming weeks and months will follow.</p><p>The user-oriented evaluation and design of this climate service has been developed in close cooperation with users from various sectors at workshops of the German MiKlip project and will be consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with the prediction skill for 1-year and 5-year means/ sums of temperature and precipitation for different regions (World, Europe, Germany, German regions).</p><p>For Germany, the statistical downscaling EPISODES was applied to reach high spatial resolution required by several climate data users. Decadal predictions were statistically recalibrated in order to adjust bias, drift and standard deviation and optimize ensemble spread. We used the MSESS and RPSS to evaluate the skill of climate predictions in comparison to reference predictions, e.g. ‘observed climatology’ or ‘uninitialized climate projections’ (which are both applied by users until now as an alternative to climate predictions). The significance was tested via bootstraps.</p><p>Within the ‘basic climate predictions’ section, a user-oriented traffic light indicates whether regional-mean climate predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than reference predictions. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction itself (via the color of dots) and its skill (via the size of dots representing the skill categories of the traffic light). The co-development of this climate prediction application with users from different sectors strongly improves the comprehensibility and applicability by users in their daily work.</p><p>In addition to sub-seasonal and seasonal predictions, plans for future extensions of this climate service include multi-year seasonal predictions, e.g. 5-year summer or winter means, combined products for climate predictions and climate projections, further user-oriented, extreme or large-scale variables, e.g. ENSO, or high-resolution applications for German cities based on statistically downscaled predictions.</p>

Selecting climate projections for services: the DRIAS-2020 dataset

2021 ◽  
Author(s):  
Lola Corre ◽  
Samuel Somot ◽  
Jean-Michel Soubeyroux ◽  
Sébastien Bernus ◽  
Agathe Drouin ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Easy Access ◽  
Climate Projections ◽  
Climate Scenarios ◽  
Climate Data ◽  
Quantile Mapping ◽  
The Past ◽  
Climate Service ◽  
Climatic Information

<p>The French National Climate Service “Drias, futures of climate” was launched in 2012, as a response of the French scientific community to society’s need for climatic information. It is mainly composed of a website that provides easy access to the best available climate data to characterize climate change over France. Latest advances developed in 2020 include the availability of a new set of regional climate scenarios corrected by a quantile-mapping based method with correction depending on the weather regime. As for the previous set, the climate projections are based on the EURO-CORDEX ensemble, whose contents have been greatly enriched over the past years. Singular effort was done to build a robust and synthetic set that well represents the uncertainties of climate change over France. The different criteria defined to select the simulations will be presented, and the range of the projected climate change will be examined, with respect to larger ensembles.</p><p> </p>

scholarly journals Assessing Annual Actual Evapotranspiration Based on Climate, Topography and Soil in Natural and Agricultural Ecosystems

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 20
Author(s):  
Kleoniki Demertzi ◽  
Vassilios Pisinaras ◽  
Emanuel Lekakis ◽  
Evangelos Tziritis ◽  
Konstantinos Babakos ◽  
...  
Keyword(s):  
Water Balance ◽  
Large Scale ◽  
Hydrological Model ◽  
Actual Evapotranspiration ◽  
Irrigated Agriculture ◽  
Climate Data ◽  
Natural Ecosystems ◽  
Loading Effects ◽  
Very High ◽  
Of Greece

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.

Equilibrium Spin-up of Cold and Warm Permafrost Models

2021 ◽  
Author(s):  
Cameron Ross ◽  
Ryley Beddoe ◽  
Greg Siemens
Keyword(s):  
Climate Change ◽  
Temperature Response ◽  
Ground Temperature ◽  
Future Climate Change ◽  
Climate Projections ◽  
Multiple Model ◽  
Climate Data ◽  
Ground Temperatures ◽  
C Cycle ◽  
Warm Permafrost

<p>Initialization (spin-up) of a numerical ground temperature model is a critical but often neglected step for solving heat transfer problems in permafrost. Improper initialization can lead to significant underlying model drift in subsequent transient simulations, distorting the effects on ground temperature from future climate change or applied infrastructure.  In a typical spin-up simulation, a year or more of climate data are applied at the surface and cycled repeatedly until ground temperatures are declared to be at equilibrium with the imposed boundary conditions, and independent of the starting conditions.</p><p>Spin-up equilibrium is often simply declared after a specified number of spin-up cycles. In few studies, equilibrium is visually confirmed by plotting ground temperatures vs spin-up cycles until temperatures stabilize; or is declared when a certain inter-cycle-temperature-change threshold is met simultaneously at all depths, such as ∆T ≤ 0.01<sup>o</sup>C per cycle. In this study, we investigate the effectiveness of these methods for determining an equilibrium state in a variety of permafrost models, including shallow and deep (10 – 200 m), high and low saturation soils (S = 100 and S = 20), and cold and warm permafrost (MAGT = ~-10 <sup>o</sup>C and >-1 <sup>o</sup>C). The efficacy of equilibrium criteria 0.01<sup>o</sup>C/cycle and 0.0001<sup>o</sup>C/cycle are compared. Both methods are shown to prematurely indicate equilibrium in multiple model scenarios.  Results show that no single criterion can programmatically detect equilibrium in all tested models, and in some scenarios can result in up to 10<sup>o</sup>C temperature error or 80% less permafrost than at true equilibrium.  A combination of equilibrium criteria and visual confirmation plots is recommended for evaluating and declaring equilibrium in a spin-up simulation.</p><p>Long-duration spin-up is particularly important for deep (10+ m) ground models where thermal inertia of underlying permafrost slows the ground temperature response to surface forcing, often requiring hundreds or even thousands of spin-up cycles to establish equilibrium. Subsequent transient analyses also show that use of a properly initialized 100 m permafrost model can reduce the effect of climate change on mean annual ground temperature of cold permafrost by more than 1 <sup>o</sup>C and 3 <sup>o</sup>C under RCP2.6 and RCP8.5 climate projections, respectively, when compared to an identical 25 m model. These results have important implications for scientists, engineers and policy makers that rely on model projections of long-term permafrost conditions.</p>

scholarly journals The Decadal Climate Prediction Project

2016 ◽  
Author(s):  
George J. Boer ◽  
Douglas M . Smith ◽  
Christophe Cassou ◽  
Francisco Doblas-Reyes ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Climate Prediction ◽  
Decadal Prediction ◽  
Science And Society ◽  
Climate Data ◽  
Climate Shifts ◽  
Component C ◽  
Decadal Climate ◽  
Decadal Climate Prediction ◽  
Ensemble Generation

Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from CMIP5 and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as part of CMIP6. The DCPP consists of three Components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, dissemination and analysis of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the "hiatus", volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the Components of the DCPP, each of which are separately prioritized, as are of interest to them. The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.

scholarly journals Semi-automated calibration method for modelling of mountain permafrost evolution in Switzerland

2016 ◽  
Vol 10 (6) ◽  
pp. 2693-2719 ◽  
Author(s):  
Antoine Marmy ◽  
Jan Rajczak ◽  
Reynald Delaloye ◽  
Christin Hilbich ◽  
Martin Hoelzle ◽  
...  
Keyword(s):  
Large Scale ◽  
Calibration Method ◽  
Swiss Alps ◽  
Climate Projections ◽  
European Alps ◽  
Permafrost Degradation ◽  
Future Evolution ◽  
Mountainous Regions ◽  
Automated Calibration ◽  
The Future

Abstract. Permafrost is a widespread phenomenon in mountainous regions of the world such as the European Alps. Many important topics such as the future evolution of permafrost related to climate change and the detection of permafrost related to potential natural hazards sites are of major concern to our society. Numerical permafrost models are the only tools which allow for the projection of the future evolution of permafrost. Due to the complexity of the processes involved and the heterogeneity of Alpine terrain, models must be carefully calibrated, and results should be compared with observations at the site (borehole) scale. However, for large-scale applications, a site-specific model calibration for a multitude of grid points would be very time-consuming. To tackle this issue, this study presents a semi-automated calibration method using the Generalized Likelihood Uncertainty Estimation (GLUE) as implemented in a 1-D soil model (CoupModel) and applies it to six permafrost sites in the Swiss Alps. We show that this semi-automated calibration method is able to accurately reproduce the main thermal condition characteristics with some limitations at sites with unique conditions such as 3-D air or water circulation, which have to be calibrated manually. The calibration obtained was used for global and regional climate model (GCM/RCM)-based long-term climate projections under the A1B climate scenario (EU-ENSEMBLES project) specifically downscaled at each borehole site. The projection shows general permafrost degradation with thawing at 10 m, even partially reaching 20 m depth by the end of the century, but with different timing among the sites and with partly considerable uncertainties due to the spread of the applied climatic forcing.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

scholarly journals Homogeneity Test and Correction of Daily Temperature and Precipitation Data (1978–2015) in North China

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Lingling Shen ◽  
Li Lu ◽  
Tianjie Hu ◽  
Runsheng Lin ◽  
Ji Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
North China ◽  
Maximum Temperature ◽  
Homogeneity Test ◽  
Climate Data ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Daily Data ◽  
Before And After ◽  
Annual Average Temperature

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978–2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. The results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. The stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. The numbers of breakpoints in the daily precipitation series in North China during 1978–2015 also culminated in 2000. The reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. The annual precipitation declines by 0.96 mm. The overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978–2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

scholarly journals Learning for Teaching: Building Professional Knowledge on a National Scale

2009 ◽  
Vol 35 (1) ◽  
Author(s):  
Elizabeth Hartnell-Young
Keyword(s):  
Present Article ◽  
Large Scale ◽  
National Scale ◽  
Daily Work ◽  
School Communities ◽  
National Quality ◽  
Working Together ◽  
Structure Sociale ◽  
Under Performing

This paper takes a large-scale social perspective in describing a national project in Australia that was premised on local school communities working together and contributing ideas for the benefit of their students, and potentially, the whole country. The project was intended to improve schools’ capacity for educating boys, and in the long-term, the learning outcomes of under-performing boys, using evidence-based and action research methods. It was supported by the web spaces and tools of the National Quality Schooling Framework and Think.com. This paper emphasises the structures and processes teachers engaged in while building knowledge through their daily work, where the resulting ideas became the property of the whole community. Analyses focus on the extent to which an underlying social structure for knowledge building developed in various parts of the nation during the project, making it possible to characterize a process for innovations in education with commitment to continual idea improvement. Résumé Le présent article adopte une perspective sociale à grande échelle pour décrire un projet national en Australie fondé sur la collaboration des communautés scolaires locales et leur contribution d’idées au bénéfice de leurs élèves et, éventuellement, de l’ensemble du pays. Le projet avait pour but de rendre les écoles plus aptes à éduquer les garçons et, à long terme, d’améliorer les résultats d’apprentissage des garçons qui sous-performent à l’aide de méthodes de recherche-action fondées sur des données probantes. Il a bénéficié du soutien des espaces et des outils Web du National Quality Schooling Framework et de Think.com. Le présent article met l’accent sur les structures et les processus que les enseignants ont utilisés dans leur travail quotidien pour la coélaboration de connaissances; les idées qui en ont résulté sont par la suite devenues la propriété de l’ensemble de la communauté. Les analyses portent principalement sur la mesure dans laquelle une structure sociale sous-jacente de coélaboration des connaissances s’est développée en différents endroits de la nation au cours du projet, ce qui rend possible la caractérisation d’un processus d’innovation en éducation avec un engagement envers l’amélioration continue.

scholarly journals Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

2014 ◽  
Vol 1 (1) ◽  
pp. 51-96 ◽  
Author(s):  
A. R. Ganguly ◽  
E. A. Kodra ◽  
A. Banerjee ◽  
S. Boriah ◽  
S. Chatterjee ◽  
...  
Keyword(s):  
Data Mining ◽  
Heat Waves ◽  
Climate Extremes ◽  
Computer Models ◽  
Climate Projections ◽  
Small Data ◽  
Climate Data ◽  
Statistics Of Extremes ◽  
Data Mining Techniques ◽  
Grand Challenges

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems, and human communities, worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "Big Data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically-based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

scholarly journals Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Eleanor Blyth ◽  
Hollie Cooper ◽  
Rich Ellis ◽  
Ewan Pinnington ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Climate Projections ◽  
Land Surface Models ◽  
Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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