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).     

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.     

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.     

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.     

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.     

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.     

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.     

Feedback and sensitivity in an electrical circuit: an analog for climate models

Earth’s climate sensitivity is often interpreted in terms of feedbacks that can alter the sensitivity from that of a no-feedback Stefan-Boltzmann radiator, with the feedback concept and algebra introduced by analogy to the use of this concept in the electronics literature. This analogy is quite valuable in interpreting the sensitivity of the climate system, but usage of this algebra and terminology in the climate literature is often inconsistent, with resultant potential for confusion and loss of physical insight. Here a simple and readily understood electrical resistance circuit is examined in terms of feedback theory to introduce and define the terminology that is used to quantify feedbacks. This formalism is applied to the feedbacks in an energy-balance model of Earth’s climate and used to interpret the magnitude of feedback in the climate system that corresponds to present estimates of Earth’s climate sensitivity.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

CLIMBER-2: a climate system model of intermediate complexity. Part I: model description and performance for present climate

A 2.5-dimensional climate system model of intermediate complexity CLIMBER-2 and its performance for present climate conditions are presented. The model consists of modules describing atmosphere, ocean, sea ice, land surface processes, terrestrial vegetation cover, and global carbon cycle. The modules interact through the fluxes of momentum, energy, water and carbon. The model has a coarse spatial resolution, nevertheless capturing the major features of the Earth's geography. The model describes temporal variability of the system on seasonal and longer time scales. Due to the fact that the model does not employ flux adjustments and has a fast turnaround time, it can be used to study climates significantly different from the present one and to perform long-term (multimillennia) simulations. The comparison of the model results with present climate data show that the model successfully describes the seasonal variability of a large set of characteristics of the climate system, including radiative balance, temperature, precipitation, ocean circulation and cryosphere. Received: 12 January 1998 / Accepted: 2 July 1999     

The benefits of climate change mitigation in integrated assessment models: the role of the carbon cycle and climate component

Integrated Assessment Models (IAMs) are an important tool to compare the costs and benefits of different climate policies. Recently, attention has been given to the effect of different discounting methods and damage estimates on the results of IAMs. One aspect to which little attention has been paid is how the representation of the climate system may affect the estimated benefits of mitigation action. In that respect, we analyse several well-known IAMs, including the newest versions of FUND, DICE and PAGE. Given the role of IAMs in integrating information from different disciplines, they should ideally represent both best estimates and the ranges of anticipated climate system and carbon cycle behaviour (as e.g. synthesised in the IPCC Assessment reports). We show that in the longer term, beyond 2100, most IAM parameterisations of the carbon cycle imply lower CO₂ concentrations compared to a model that captures IPCC AR4 knowledge more closely, e.g. the carbon-cycle climate model MAGICC6. With regard to the climate component, some IAMs lead to much lower benefits of mitigation than MAGICC6. The most important reason for the underestimation of the benefits of mitigation is the failure in capturing climate dynamics correctly, which implies this could be a potential development area to focus on.     

Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models

A method for simulating future climate on regional space scales is developed and applied to northern Africa. Simulation with a regional model allows for the horizontal resolution needed to resolve the region’s strong meridional gradients and the optimization of parameterizations and land-surface model. The control simulation is constrained by reanalysis data, and realistically represents the present day climate. Atmosphere–ocean general circulation model (AOGCM) output provides SST and lateral boundary condition anomalies for 2081–2100 under a business-as-usual emissions scenario, and the atmospheric CO₂ concentration is increased to 757 ppmv. A nine-member ensemble of future climate projections is generated by using output from nine AOGCMs. The consistency of precipitation projections for the end of the twenty-first century is much greater for the regional model ensemble than among the AOGCMs. More than 77% of ensemble members produce the same sign rainfall anomaly over much of northern Africa. For West Africa, the regional model projects wetter conditions in spring, but a mid-summer drought develops during June and July, and the heat stoke risk increases across the Sahel. Wetter conditions resume in late summer, and the likelihood of flooding increases. The regional model generally projects wetter conditions over eastern Central Africa in June and drying during August through September. Severe drought impacts parts of East Africa in late summer. Conditions become wetter in October, but the enhanced rainfall does not compensate for the summertime deficit. The risk of heat stroke increases over this region, although the threat is not projected to be as great as in the Sahel.     

Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models

 This study compares radiative fluxes and cloudiness fields from three general circulation models (the HadAM4 version of the Hadley Centre Unified model, cycle 16r2 of the ECMWF model and version LMDZ 2.0 of the LMD GCM), using a combination of satellite observations from the Earth Radiation Budget Experiment (ERBE) and the International Satellite Cloud Climatology Project (ISCCP). To facilitate a meaningful comparison with the ISCCP C1 data, values of column cloud optical thickness and cloud top pressure are diagnosed from the models in a manner consistent with the satellite view from space. Decomposing the cloud radiative effect into contributions from low-medium- and high-level clouds reveals a tendency for the models' low-level clouds to compensate for underestimates in the shortwave cloud radiative effect caused by a lack of high-level or mid-level clouds. The low clouds fail to compensate for the associated errors in the longwave. Consequently, disproportionate errors in the longwave and shortwave cloud radiative effect in models may be taken as an indication that compensating errors are likely to be present. Mid-level cloud errors in the mid-latitudes appear to depend as much on the choice of the convection scheme as on the cloud scheme. Convective and boundary layer mixing schemes require as much consideration as cloud and precipitation schemes when it comes to assessing the simulation of clouds by models. Two distinct types of cloud feedback are discussed. While there is reason to doubt that current models are able to simulate potential `cloud regime' type feedbacks with skill, there is hope that a model capable of simulating potential `cloud amount' type feedbacks will be achievable once the reasons for the remaining differences between the models are understood. Received: 23 January 2000 / Accepted: 24 January 2001     

Testing for trend in variability of climate data: measures and temporal aggregation with applications to Canadian data

Summary It is not clear whether different measures of dispersion of weather attributes could lead to different conclusions on the nature and direction of climatic variability. The range is commonly used as a measure of variability, while the presence of trend is typically studied on seasonal and/or annual basis. In this study, we used daily average temperature values at 15 sites spatially distributed across Canada to test for the presence of trend in variability (measured by the range, the standard deviation and the IQR) using a bootstrap method. The length of the series varied from site to site, ranging from 30 to 151 years. The analysis was undertaken for each month, each season, and the annual data. When calculating the standard deviations, estimates of the annual mean temperatures were used to make the results invariant to the presence of trend in mean. The monthly and seasonal analysis revealed the presence of either increasing or decreasing variability for some months and some seasons. The results for the annual data were not so revealing, especially at sites where some months have increasing while others have decreasing trends. The results across sites did not exhibit a clear geographic pattern. However, there were consistently increasing trends in variability at Toronto and St. Johns during non-summer months, and mostly decreasing trend in Edmonton. The significance of trend in variability using the range and the standard deviation were consistent in less than 30% of the time across sites and across the monthly, seasonal and annual aggregations. There was not much agreement between the standard deviation and the IQR either, highlighting the importance of the choice of a measure of variability.     

黔西县菊花的适应性分析及最佳种植气候模式

根据菊花的生态学特征,结合菊花单产的高低,对照黔西县的主要气象要素,得出了适合菊花高产高质的生长发育关键期的气温、日照、降水条件。