Thirteen regional climate model(RCM) simulations of June - July 1993 were compared with each other and observations. Water vapor conservation and precipitation characteristics in each RCM were examined for a 108 x 10degrees subregion of the upper Mississippi River basin, containing the region of maximum 60-day accumulated precipitation in all RCMs and station reports. All RCMs produced positive precipitation minus evapotranspiration ( P - E > 0), though most RCMs produced P - E below the observed range. RCM recycling ratios were within the range estimated from observations. No evidence of common errors of E was found. In contrast, common dry bias of P was found in the simulations. Daily cycles of terms in the water vapor conservation equation were qualitatively similar in most RCMs. Nocturnal maximums of P and C ( convergence) occurred in 9 of 13 RCMs, consistent with observations. Three of the four driest simulations failed to couple P and C overnight, producing afternoon maximum P. Further, dry simulations tended to produce a larger fraction of their 60-day accumulated precipitation from low 3-h totals. In station reports, accumulation from high ( low) 3-h totals had a nocturnal ( early morning) maximum. This time lag occurred, in part, because many mesoscale convective systems had reached peak intensity overnight and had declined in intensity by early morning. None of the RCMs contained such a time lag. It is recommended that short-period experiments be performed to examine the ability of RCMs to simulate mesoscale convective systems prior to generating long-period simulations for hydroclimatology.

This article presents the design and testing of a model-assisted participatory process for the formulation of a local adaptation plan to climate change. The pilot study focused on small-scale and commercial agriculture, water supply, housing, wildlife, livestock and biodiversity in the Thukela River basin, KwaZulu-Natal, South Africa. The methodology was based on stakeholders identifying and ranking the severity of climate-related challenges, and downscaled stakeholder-identified information provided by modellers, with the aim of addressing possible changes of exposure in the future. The methodology enables the integration of model-based information with experience and visions based on local realities. It includes stakeholders' own assessments of their vulnerability to prevailing climate variability and the severity, if specified, of climate-related problems that may occur more often in the future. The methodology made it possible to identify the main issues to focus on in the adaptation plan, including barriers to adaptation. We make recommendations for how to design a model-assisted participatory process, emphasizing the need for transparency, to recognize the interests of the stakeholders, good advance planning, local relevance, involvement of local champions, and adaptation of Information material to each group's previous experience and understanding.

Ett klimatologiskt griddat datasett (SMHI Gridded Climatology - SMHIGridClim) hartagits fram för åren 1961 – 2018. Data täcker de nordiska länderna med en horisontellupplösning av 2,5 km. Variablerna som tagits fram är lufttemperatur och relativluftfuktighet vid 2m höjd med en upplösning av1,3 eller 6 timmar beroende av tidsperiod,samt dygnsupplöst min- och maxtemperatur, nederbörd och snödjup. Datasetet ärframtaget med optimal interpolation av stationsdata genom analysverktyget gridpp, somär en öppet tillgänglig programvara från Norska Meteorologiska Institutet.Observationer till analysen har erhållits från de svenska, norska och finskameteorologiska instituten, samt ECMWF. En ansats gjordes också att användaobservationer från datasetet ECA&D från KNMI, men på grund av svårigheter med atttidsstämplarna för data från olika länder inte överensstämde, uteslöts datasetet uranalysen. Kvalitetskontroll av observationerna gjordes med programvaran TITAN, somäven den finns tillgänglig från och utvecklats av Norska Meteorologiska Institutet.Som en första gissning till interpolationen användes statistiskt nerskalade prognosfält(från 11 km till 2,5 km upplösning) från UERRA-HARMONIE. Nerskalningen gjordesmot fält från den operationella numeriska väderprognosmodellen MEPS. Anpassningengjordes med nedskalningsparametrar som varierar över året och dygnet.Kvalitén hos ”SMHIGridClim med avseende på genomsnittligt RMSE är liknande densom tagits fram för griddade data för andra nordiska länderna med varierandeanalysmetoder; “seNorge” från Norge och “FMI_ClimGrid” från Finland.

Climate change resulting from the enhanced greenhouse effect is expected to give rise to changes in hydrological systems. This hydrological change, as with the change in climate variables, will vary regionally around the globe. Impact studies at local and regional scales are needed to assess how different regions will be affected. This study focuses on assessment of hydrological impacts of climate change over a wide range of Swedish basins. Different methods of transferring the signal of climate change from climate models to hydrological models were used. Several hydrological model simulations using regional climate model scenarios from Swedish Regional Climate Modelling Programme (SWECLIM) are presented. A principal conclusion is that subregional impacts to river flow vary considerably according to whether a basin is in northern or southern Sweden. Furthermore, projected hydrological change is just as dependent on the choice of the global climate model used for regional climate model boundary conditions as the choice of anthropogenic emissions scenario.

Objectives: Respiratory diseases are ranked second in Europe in terms of mortality, prevalence and costs. Studies have shown that extreme heat has a large impact on mortality and morbidity, with a large relative increase for respiratory diseases. Expected increases in mean temperature and the number of extreme heat events over the coming decades due to climate change raise questions about the possible health impacts. We assess the number of heat-related respiratory hospital admissions in a future with a different climate. Design: A Europe-wide health impact assessment. Setting: An assessment for each of the EU27 countries. Methods: Heat-related hospital admissions under a changing climate are projected using multicity epidemiological exposure-response relationships applied to gridded population data and country-specific baseline respiratory hospital admission rates. Times-series of temperatures are simulated with a regional climate model based on four global climate models, under two greenhouse gas emission scenarios. Results: Between a reference period (1981-2010) and a future period (2021-2050), the total number of respiratory hospital admissions attributed to heat is projected to be larger in southern Europe, with three times more heat attributed respiratory hospital admissions in the future period. The smallest change was estimated in Eastern Europe with about a twofold increase. For all of Europe, the number of heat-related respiratory hospital admissions is projected to be 26 000 annually in the future period compared with 11 000 in the reference period. Conclusions: The results suggest that the projected effects of climate change on temperature and the number of extreme heat events could substantially influence respiratory morbidity across Europe.

Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021-2050 and 2071-2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3 degrees C in the first period and 2 and 5 degrees C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of +1 to -1 mm/day. Regional features are amplified by the RCMs, more so in case of the ALADIN family of models.

We review recent progress in understanding the role of sea ice, land surface, stratosphere, and aerosols in decadal-scale predictability and discuss the perspectives for improving the predictive capabilities of current Earth system models (ESMs). These constituents have received relatively little attention because their contribution to the slow climatic manifold is controversial in comparison to that of the large heat capacity of the oceans. Furthermore, their initialization as well as their representation in state-of-the-art climate models remains a challenge. Numerous extraoceanic processes that could be active over the decadal range are proposed. Potential predictability associated with the aforementioned, poorly represented, and scarcely observed constituents of the climate system has been primarily inspected through numerical simulations performed under idealized experimental settings. The impact, however, on practical decadal predictions, conducted with realistically initialized full-fledged climate models, is still largely unexploited. Enhancing initial-value predictability through an improved model initialization appears to be a viable option for land surface, sea ice, and, marginally, the stratosphere. Similarly, capturing future aerosol emission storylines might lead to an improved representation of both global and regional short-term climatic changes. In addition to these factors, a key role on the overall predictive ability of ESMs is expected to be played by an accurate representation of processes associated with specific components of the climate system. These act as signal carriers, transferring across the climatic phase space the information associated with the initial state and boundary forcings, and dynamically bridging different (otherwise unconnected) subsystems. Through this mechanism, Earth system components trigger low-frequency variability modes, thus extending the predictability beyond the seasonal scale.

A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.

Sverige var ett föregångsland inom numeriska vädderprognoser och den allra första operativa väderprognosen gjordes redan 1954 på det Internationella Meteorologiska Institutet i Stockholm. SMHI kom igång senare, men 1961 startade man ett långsiktigt program för NWP (numerical weather prediction). Projektet växte gradvis under 1960-talet och blev så småningom en central komponent i SMHIs prognostjänst. En utmaning under de tidiga åren var de begränsade dataresurserna med primitiv programvara, och med dagens mått begränsat minnesutrymme och låg beräkningshastighet. För att kompensera dessa brister krävdes både beslutsamhet och ett stort mått av kreativitet. Som en central komponent i arbetet utvecklade NWP-gruppen datorsystemet MAC (Meteorological Auto Code) som här beskrivs i detalj samt också alla de beräkningsprogram som krävdes för prognostjänsten. Detta inkluderade olika prognosmodeller, analys samt program för databehandling och observationskontroll samt produktion av prognosresultaten i grafisk eller digital form.

Det är vår förhoppning att f´öreliggande artikel skall ge den yngre generationen en inblick i hur det var att syssla med NWP under 1960-talet.

Biasjustering används för att anpassa resultaten från klimatmodeller så att de blir användbara för effektmodellering och beräkning av klimatindikatorer. Klimatmodeller uppvisar nämligen regionala och säsongsbetonade systematiska avvikelser från observerat klimat, vilket leder till problem för effektmodeller som är kalibrerade mot observationer. Biasjusteringen består i grunden av en algoritm som beskriver hur olika värden av till exempel temperatur eller nederbörd justeras för att återskapa en långsiktig statistisk beskrivning av klimatet enligt observationerna. Metoden MIdAS (MultI-scale bias AdjuStment) har tagits fram för biasjustering inom SMHI. En avdelningsöverskridande arbetsgrupp har genom litteraturstudie tagit fram de metoder som ligger närmast en internationell “state-of-the-art” inom biasjustering. Fokus har varit på justering av de huvudsakliga parametrar som används inom SMHI för att producera klimatindikatorer och för effektmodellering inom främst hydrologi. En utvärderingsmetodik har tagits fram för att studera olika metoders resultat för historiska och framtida data, i Sverige och olika regioner i världen. Resultaten av studien visar på att enkla metoder fungerar likvärdigt eller bättre än de mer komplexa metoderna, förutom en större påverkanpå klimatsignalers magnitud i vissa situationer. Implementeringen av MIdASv0.1 är likvärdig och ofta bättre än andra metoder.

The performance of the Rossby Centre regional climate model RCA4 is investigated for the Arctic CORDEX (COordinated Regional climate Downscaling EXperiment) region, with an emphasis on its suitability to be coupled to a regional ocean and sea ice model. Large biases in mean sea level pressure (MSLP) are identified, with pronounced too-high pressure centred over the North Pole in summer of over 5 hPa, and too-low pressure in winter of a similar magnitude. These lead to biases in the surface winds, which will potentially lead to strong sea ice biases in a future coupled system. The large-scale circulation is believed to be the major reason for the biases, and an implementation of spectral nudging is applied to remedy the problems by constraining the large-scale components of the driving fields within the interior domain. It is found that the spectral nudging generally corrects for the MSLP and wind biases, while not significantly affecting other variables, such as surface radiative components, two-metre temperature and precipitation.

Bias correction of varying complexity - from simple scaling and additive corrections to more advanced histogram equalisation (HE) corrections - is applied to high resolution (7 km) regional climate model (RCM) simulations. The aim of the study is to compare different methods that are easily implemented and applied to the data, and to assess the applicability and impact of the bias correction depending on the type of bias. The model bias is determined by comparison to a new gridded high resolution (1 km) data set of temperature and precipitation, which is also used as reference for the corrections. The performance of the different methods depends on the type of bias of the model, and on the investigated statistic. Whereas simpler methods correct the first moment of the distributions, they can have adverse effects on higher moments. The HE method corrects also higher moments, but approximations of the transfer function are necessary when applying the method to other data than the calibration data. Here, an empirical transfer function with linear fits to the tails is compared to a version where the complete function is approximated by a linear fit. The latter is thus limited to corrections of the first and second moments of the distribution. While making the transfer function more generally applicable, these approximations also limit the performance of the HE method. For the current model biases, the linear approximation is found suitable for precipitation, but for temperature it is not able to correct the whole distribution. The lower performance of the linear correction is most pronounced in summer, and is likely due to a difference in skewness between the model and observational data. Further limitations of the HE method are due to the need for long time series in order to have robust distributions for calculating the transfer function. Theoretical approximations of the required length of the calibration period were performed by using different sampling sizes drawn from a known distribution. The excerise show that about 30 year long time series are needed to have reasonable accuracy for the estimation of variance, when also corrections of the annual cycle is required. (C) 2012 Elsevier B.V. All rights reserved.

Precipitation changes can affect society more directly than variations in most other meteorological observables(1-3), but precipitation is difficult to characterize because of fluctuations on nearly all temporal and spatial scales. In addition, the intensity of extreme precipitation rises markedly at higher temperature(4-9), faster than the rate of increase in the atmosphere's water-holding capacity(1,4), termed the Clausius-Clapeyron rate. Invigoration of convective precipitation (such as thunderstorms) has been favoured over a rise in strati-form precipitation (such as large-scale frontal precipitation) as a cause for this increase(4,10), but the relative contributions of these two types of precipitation have been difficult to disentangle. Here we combine large data sets from radar measurements and rain gauges over Germany with corresponding synoptic observations and temperature records, and separate convective and stratiform precipitation events by cloud observations. We find that for stratiform precipitation, extremes increase with temperature at approximately the Clausius-Clapeyron rate, without characteristic scales. In contrast, convective precipitation exhibits characteristic spatial and temporal scales, and its intensity in response to warming exceeds the Clausius-Clapeyron rate. We conclude that convective precipitation responds much more sensitively to temperature increases than stratiform precipitation, and increasingly dominates events of extreme precipitation.

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

The Swedish regional climate modelling programme, SWECLIM, started in 1997 with the main goal being to produce regional climate change scenarios over the Nordic area on a time scale of 50 to 100 yr. An additional goal is to produce water resources scenarios with a focus on hydropower production, dam safety, water supply and environmental aspects of water resources. The scenarios are produced by a combination of global climate models (GCMs), regional climate models and hydrological runoff models. The GCM simulations used thus far are 10 yr time slices from 2 different GCMs, UKMO HadCM2 from the Hadley Centre and the ECHAM4/OPYC3 of the Max Planck Institute for Meteorology. The regional climate model is a modified version of the international HIRLAM forecast model and the hydrological model is the HBV model developed at the Swedish Meteorological and Hydrological Institute. Scenarios of river runoff have been simulated for 6 selected basins covering the major climate regions in Sweden. Changes in runoff totals, runoff regimes and extreme values have been analysed with a focus on the uncertainties introduced by the choice of GCM and routines for estimation of evapotranspiration in the hydrological model. It is further shown how these choices affect the statistical return periods of future extremes in a design situation.

The problem of scales and particularly the modelling of macro or continental scale catchments in hydrology is addressed. It is concluded that the magnitude of the scale problem is related to the specific hydrologic problem to be solved and to the scientific approach and perspective of the modeller. A distributed modelling approach, based on variability parameters, is suggested for modelling of soil moisture dynamics and runoff generation. It is shown that the parameters of such an approach are relatively stable over a wide range of scales. An example of the application of a standard Version of the Swedish HBV hydrological model to the continental scale catchment of the Baltic Sea is shown and its usefulness is discussed. (C) 1998 Elsevier Science Ltd. All rights reserved.

In this study, we developed models of transfer effects for growth and survival of Scots pine (Pinus sylvestris L.) in Sweden and Finland using a general linear mixed-model approach. For model development, we used 378 provenance and progeny trials with a total of 276 unimproved genetic entries (provenances and stand seed check-lots) distributed over a wide variety of climatic conditions in both countries. In addition, we used 119 progeny trials with 3921 selected genetic entries (open-and control pollinated plus-tree families) for testing model performance. As explanatory variables, both climatic indices derived from high-resolution gridded climate datasets and geographical variables were used. For transfer, latitude (photoperiod) and, for describing the site, temperature sum were found to be main drivers for both survival and growth. In addition, interaction terms (between transfer in latitude and site altitude for survival, and transfer in latitude and temperature sum for growth) entail changed reaction patterns of the models depending on climatic conditions of the growing site. The new models behave in a way that corresponds well to previous studies and recommendations for both countries. The model performance was tested using selected plus-trees from open and control pollinated progeny tests. Results imply that the models are valid for both countries and perform well also for genetically improved material. These models are the first step in developing common deployment recommendations for genetically improved forest regeneration material in both Sweden and Finland.