Spatial interpolation of monthly and annual rainfall in northeast of Iran

Precipitation maps are the key input to many hydrological models. In this paper different univariate (inverse distance weighing and ordinary kriging) and multivariate (linear regression, ordinary cokriging, simple kriging with varying local mean and kriging with an external drift) interpolation methods are used to map monthly and annual rainfall from sparse data measurements. The study area is Golestan Province, located in northeast of Iran. A digital elevation model is used as complementary information for multivariate approaches. The prediction performance of each method is evaluated through cross-validation and visual examination of the precipitation maps produced. Results indicate that geostatistical algorithms clearly outperform inverse distance weighting and linear regression. Among multivariate techniques, ordinary cokriging or kriging with an external drift yields the smallest error of prediction for months April to October (autumn and winter) for which the correlation between rainfall and elevation is greater than 0.54. For all other months and annual rainfall, ordinary kriging provides the most accurate estimates.     

An inter-comparison of regional climate models for Europe: model performance in present-day climate

The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.     

Some mechanisms of mid-Holocene climate change in Europe,inferred from comparing PMIP models to data

We propose a new approach for comparing mid-Holocene climates from 18 PMIP simulations with climate reconstructions of winter and growing season temperatures and the annual water budget inferred from European pollen and lake-level data. A cluster analysis is used to extract patterns of multivariate climate response from the reconstructions; these are then compared to the patterns simulated by models. According to paleodata, summers during mid-Holocene were warmer-than-present in the north, and cooler-than-present in the south, while winters were colder-than-present in the southwest but milder-than-present in the northeast. Whereas warmer summers and colder winters may easily be explained as a direct response to the amplified seasonal cycle of insolation during the mid-Holocene, the other recorded responses are less straightforward to explain. We have identified, from the models that correctly simulate the recorded climate change, two important atmospheric and hydrological processes that can compensate for direct insolation effects. First, a stronger-than-present airflow from southwestern Europe that veers to the north over Eastern Europe, in winter, can consistently explain the reconstructed changes in this seasons temperatures and water budget. Second, the increased winter soil moisture allows a shift of the partitioning of net radiative energy towards latent rather than sensible heat fluxes, thereby decreasing surface temperature during the following summer season. Our approach therefore solves one of the recurring problems in model-data comparisons that arises when a model simulates the correct response but in the wrong location (as a consequence, for instance, of model resolution and topography).     

Building a regime for climate protection: Finland and international climate policy

The effective management of climate change on a national as well as an international level requires close co-operation between the scientific community and the political sector. Climate change first became a major subject of scientific inquiry in the early 1980s, and real political interest in the issue was awakened towards the end of that decade. In the last few years, the dialogue between the scientific community and the political sector has increased considerably. As a result, climate change is today one of the most significant areas in environmental research and international environmental policy alike.This paper examines the emergence and consolidation of international climate change regime. The theory of regime building is used as discussed by Young (1989). International Cooperation. Cornell University Press, Ithaca. The paper begins with an outline of the historical emergence of climate research and climate policy. This is followed by a discussion of the history and development of the Rio Convention, with a look at the latest developments in international climate politics. The next section begins with an examination of the foundations and present strategies of Finnish climate policy, which is used as a case study, and the paper concludes with an assessment of the current state of Finnish climate policy, illustrating the problems of compliance individual countries face when adopting the norms and principles of the regime.     

A theoretical framework for the sampling error variance for three-dimensional climate averages of ICOADS monthly ship data

Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error.     

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro,Brazil

Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r ²), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r ²) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability.     

Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.     

Predictability of Mediterranean climate variables from oceanic variability. Part II: Statistical models for monthly precipitation and temperature in the Mediterranean area

The objective of this study is to investigate the predictability of monthly climate variables in the Mediterranean area by using statistical models. It is a well-known fact that the future state of the atmosphere is sensitive to preceding conditions of the slowly varying ocean component with lead times being sufficiently long for predictive assessments. Sea surface temperatures (SSTs) are therefore regarded as one of the best variables to be used in seasonal climate predictions. In the present study, SST-regimes which have been derived and discussed in detail in Part I of this paper, are used with regard to monthly climate predictions for the Mediterranean area. Thus, cross-correlations with time lags from 0 up to 12?months and ensuing multiple regression analyses between the large-scale SST-regimes and monthly precipitation and temperature for Mediterranean sub-regions have been performed for the period 1950?C2003. Statistical hindcast ensembles of Mediterranean precipitation including categorical forecast skill can be identified only for some months in different seasons and for some individual regions of the Mediterranean area. Major predictors are the tropical Atlantic Ocean and the North Atlantic Ocean SST-regimes, but significant relationships can also be found with tropical Pacific and North Pacific SST-regimes. Statistical hindcast ensembles of Mediterranean temperature with some categorical forecast skill can be determined primarily for the Western Mediterranean and the North African regions throughout the year. As for precipitation the major predictors for temperature are located in the tropical Atlantic Ocean and the North Atlantic Ocean, but some connections also exist with the Pacific SST variations.     

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.     

Tropical storms: representation and diagnosis in climate models and the impacts of climate change

Tropical storms are located and tracked in an experiment in which a high-resolution atmosphere only model is forced with observed sea surface temperatures (SSTs) and sea ice. The structure, geographic distribution and seasonal variability of the model tropical storms show some similarities with observations. The simulation of tropical storms is better in this high-resolution experiment than in a parallel standard resolution experiment. In an anomaly experiment, sea ice, SSTs and greenhouse-gas forcing are changed to mimic the changes that occur in a coupled model as greenhouse-gases are increased. There are more tropical storms in this experiment than in the control experiment in the Northeast Pacific and Indian Ocean basins and fewer in the North Atlantic, Northwest Pacific and Southwest Pacific region. The changes in the North Atlantic and Northwest Pacific can be linked to El Niño-like behaviour. A comparison of the tracking results with two empirically derived tropical storm genesis parameters is carried out. The tracking technique and a convective genesis parameter give similar results, both in the global distribution and in the changes in the individual basins. The convective genesis parameter is also applied to parallel coupled model experiments that have a lower horizontal resolution. The changes in the global distribution of tropical storms in the coupled model experiments are consistent with the changes seen at higher resolution. This indicates that the convective genesis parameter may still provide useful information about tropical storm changes in experiments carried out with models that cannot resolve tropical storms.     

IPCC AR4多模式对海河流域气候模拟能力的评估及预估

根据海河流域1961-2010年气象观测资料,检验IPCC AR4中全球气候模式和多模式集合的模拟能力,并预估未来2011-2050年气候变化的可能趋势,结果表明:全球气候模式以及多模式集合对海河流域都具有一定的模拟能力,其中MIUB_ECHO_G模式和多模式集合具有相对较好的模拟能力.海河流域气温和降水未来情景预估表明:气温整体呈现增加趋势,尤其是A1B情景下各模式的年升温率均高于全国水平;未来降水也呈现增加趋势,在A1B和B1情景下,各模式都为夏季降水增加显著.A2情景下,春季时各模式降水均增加显著,A1B情景下,MIUB_ECHO_G模式模拟在2013年出现突变,降水量出现显著增长,A2情景下,MIUB_ECHO_G模式和多模式集合模拟的降水量则是在2031年和2001年出现突变,出现显著增长.     

Uncertainty and risk in climate projections for the 21st century: comparing mitigation to non-intervention scenarios

Probabilistic climate projections based on two SRES scenarios, an IMAGE reference scenario and five IMAGE mitigation scenarios (all of them multi-gas scenarios) using the Bern2.5D climate model are calculated. Probability distributions of climate model parameters that are constrained by observations are employed as input for the climate model. The sensitivity of the resulting distributions with respect to prior assumptions on climate sensitivity is then assessed. Due to system inertia, prior assumptions on climate sensitivity play a minor role in the case of temperature projections for the first half of the 21st century, but these assumptions have a considerable influence on the distributions of the projected temperature increase in the year 2100. Upper and lower probabilities for exceeding 2°C by the year 2100 are calculated for the different scenarios. Only the most stringent mitigation measures lead to low probabilities for exceeding the 2°C threshold. This finding is robust with respect to our prior assumptions on climate sensitivity. Further, probability distributions of total present-value damages over the period 2000–2100 for the different scenarios are calculated assuming a wide range of damage cost functions, and the sensitivity of these distributions with respect to the assumed discount rate is investigated. Absolute values of damage costs depend heavily on the chosen damage cost function and discount rate. Nevertheless, some robust conclusions are possible.     

Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin

This study was targeted at evaluating the performance of six Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX). The evaluation is on the bases of how well the RCMs simulate the seasonal mean climatology, interannual variability and annual cycles of rainfall, maximum and minimum temperature over two catchments in western Ethiopia during the period 1990–2008. Observed data obtained from the Ethiopian National Meteorological Agency was used for performance evaluation of the RCMs outputs. All Regional Climate Models (RCMs) have simulated seasonal mean annual cycles of precipitation with a significant bias shown on individual models; however, the ensemble mean exhibited better the magnitude and seasonal rainfall. Despite the highest biases of RCMs in the wet season, the annual cycle showed the prominent features of precipitation in the two catchments. In many aspects, CRCM5 and RACMO22 T simulate rainfall over most stations better than the other models. The highest biases are associated with the highest error in simulating maximum and minimum temperature with the highest biases in high elevation areas. The rainfall interannual variability is less evident in Finchaa with short rainy season experiencing a larger degree of interannual variability. The differences in performance of the Regional Climate Models in the two catchments show that all the available models are not equally good for particular locations and topographies. In this regard, the right regional climate models have to be used for any climate change impact study for local-scale climate projections.     

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.     

The Last Glacial Maximum climate over Europe and western Siberia: a PMIP comparison between models and data

 Under the framework of the Palaeoclimate Modelling Intercomparison Project (PMIP), 17 climate models, 16 of which are atmospheric general circulation models, have been run to simulate the climate of the Last Glacial Maximum (21 000 years ago) using the same set of boundary conditions. Parallel to these numerical experiments, new, consistent, data bases have been developed on a continental scale. The present work compares the range of the model responses to the large perturbation corresponding to the conditions of the Last Glacial Maximum with consistently derived climate reconstructions from pollen records over Europe and western Siberia. It accounts for the differences in the model results due to the models themselves and directly compares this “error bar” due to the models to the uncertainties in the climate reconstructions from the pollen records. Overall the Last Glacial Maximum climate simulated by the models over western Europe is warmer, especially in winter, and wetter than the one depicted by the reconstructions. This is the region where the reconstructed increase in temperature, precipitation and moisture index from the Last Glacial Maximum to the present conditions is largest. The same disagreement, but of smaller amplitude, is found over Central Europe and the eastern Mediterranean Basin, while models and data are in broad agreement over western Siberia. The numerous modelling results allow a study of the link between the changes in atmospheric circulation and those in temperature, and an interpretation of the discrepancies in precipitation in terms of those in temperature. Received: 1 February 2000 / Accepted: 9 May 2000     

Atmospheric circulation in regional climate models over Central Europe: links to surface air temperature and the influence of driving data

The study examines simulation of atmospheric circulation, represented by circulation indices (flow direction, strength and vorticity), and links between circulation and daily surface air temperatures in regional climate models (RCMs) over Central Europe. We explore control simulations of five high-resolution RCMs from the ENSEMBLES project driven by re-analysis (ERA-40) and the same global climate model (ECHAM5 GCM) plus of one RCM (RCA) driven by different GCMs. The aims are to (1) identify errors in RCM-simulated distributions of circulation indices in individual seasons, (2) identify errors in simulated temperatures under particular circulation indices, and (3) compare performance of individual RCMs with respect to the driving data. Although most of the RCMs qualitatively reflect observed distributions of the airflow indices, each produces distributions significantly different from the observations. General biases include overestimation of the frequency of strong flow days and of strong cyclonic vorticity. Some circulation biases obviously propagate from the driving data. ECHAM5 and all simulations driven by ECHAM5 underestimate frequency of easterly flow, mainly in summer. Except for HIRHAM, however, all RCMs driven by ECHAM5 improve on the driving GCM in simulating atmospheric circulation. The influence on circulation characteristics in the nested RCM differs between GCMs, as demonstrated in a set of RCA simulations with different driving data. The driving data control on circulation in RCA is particularly weak for the BCM GCM, in which case RCA substantially modifies (but does not improve) the circulation from the driving data in both winter and summer. Those RCMs with the most distorted atmospheric circulation are HIRHAM driven by ECHAM5 and RCA driven by BCM. Relatively strong relationships between circulation indices and surface air temperatures were found in the observed data for Central Europe. The links differ by season and are usually stronger for daily maxima than minima. RCMs qualitatively reproduce these relationships. Effects of the driving model biases were found on RCMs’ performance in reproducing not only atmospheric circulation but also the links to surface temperature. However, the RCM formulation appears to be more important than the driving data in representing the latter. Differences of the circulation-to-temperature links among the RCA simulations are smaller and the links tend to be more realistic compared to the driving GCMs.     

Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data,satellite estimates,and merged analyses over China

Theoretical and Applied Climatology - In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2...