Regional Climatology Research Articles

Multiscale Evaluation of Extreme Rainfall Event Predictions Using Severity Diagrams

Abstract Observations and simulations of rainfall events are usually compared by analyzing (i) the total rainfall depth produced by the event and (ii) the location of the rainfall maximum. A different approach is proposed here that compares the mesoscale simulated rainfall fields with the ground rainfall observations within the multiscale framework of maximum intensity diagrams and severity diagrams. While the first simply displays the maximum rainfall intensity of an event at a number of scales, the second gives the frequency of occurrence of the maximum rainfall intensities as a function of the spatial and temporal aggregation scales, highlighting the space–time scales of the event severity. For use in a region featuring complex relief, severity diagrams have been generalized to incorporate the regional behavior of heavy rainfall events. To assess simulation outputs from a meteorological mesoscale model, three major storms that have occurred in the last decade over a mountainous Mediterranean region of southern France are analyzed. The severity diagrams detect the critical space–time scales of the rainfall events for comparison with those predicted by the simulation. This validation approach is adapted to evaluate the ability of the mesoscale model to predict various types of storms with different regional climatologies.

A Regional Climatology of Monsoonal Precipitation in the Southwestern United States Using TRMM

Abstract Using 13 yr of data from the Tropical Rainfall Measuring Mission (TRMM) satellite, a regional climatology of monsoonal precipitation is created for portions of the southwest United States. The climatology created using precipitation features defined from the TRMM precipitation radar (PR) shows that the population of features includes a large number of small, weak features that do not produce much rain and are very shallow. A lesser percentage of large, stronger features contributes most of the region’s rainfall. Dividing the features into categories based on the median values of volumetric rainfall and maximum height of the 30-dBZ echo is a useful way to visualize the population of features, and the categories selected reflect the life cycle of monsoonal convection. An examination of the top rain-producing features at different elevations reveals that extreme features tend to occur at lower elevations later in the day. A comparison with the region studied in the North American Monsoon Experiment (NAME) shows that similar diurnal patterns occur in the Sierra Madre Occidental region of Mexico. The population of precipitation features in both regions is similar, with the NAME region producing slightly larger precipitation systems on average than the southwest United States. Both regions on occasion demonstrate the pattern of convection initiating at high elevations and moving downslope while growing upscale through the afternoon and evening; however, there are also days on which convection remains over the high terrain.

Linking atmospheric circulation to daily precipitation patterns over the territory of Bulgaria

An eight states non-homogeneous hidden Markov model is developed for linking daily precipitation amounts data at a network of 32 stations broadly covering the territory of Bulgaria to large-scale atmospheric patterns. At each site a 40-year record 1960–2000 of daily October–March precipitation amounts is modeled. The atmospheric data consists of daily sea-level pressure, geopotential height at 500 and 850 hPa, air temperature at 850 hPa and relative humidity at 700 and 850 hPa on a 2.5 × 2.5 grid based on NCEP-NCAR reanalysis data set covering the Europe-Atlantic sector 30W–60E, 20N–70N for the same period. The first 30 years data are used for model fitting purposes while the remaining 10 years are used for model evaluation. Detailed model validation is carried out on various aspects. The proposed model reproduces well the rainfall statistics for the observed and reserved data whereas the identified states are found to be physically interpretable in terms of regional climatology.

A data portal for regional climatic trend analysis in a Peruvian High Andes region

Abstract. In the frame of a Swiss-Peruvian climate change adaptation initiative (PACC), operational and historical data series of more than 100 stations of the Peruvian Meteorological and Hydrological Service (SENAMHI) are now accessible in a dedicated data portal. The data portal allows for example the comparison of data series or the interpolation of spatial fields as well as download of data in various data formats. It is thus a valuable tool supporting the process of data homogenisation and generation of a regional baseline climatology for a sound development of adequate climate change adaptation measures. The procedure to homogenize air-temperature and precipitation data series near Cusco city is outlined and followed by an exemplary trend analysis. Local air temperature trends are found to be in line with global mean trends.

Modelling surface‐air‐temperature variation over complex terrain around abisko, swedish lapland: uncertainties of measurements and models at different scales

Zhenlin, Y., Hanna, E. and Callaghan, T. V., 2011: Modelling surface‐air‐temperature variation over complex terrain around Abisko, Swedish Lapland: uncertainties of measurements and models at different scales. Geografiska Annaler: Series A, Physical Geography, 93, 89–112. DOI: 10.1111/j.1468‐0459. 2011.00005.xMany ecological, physical and geographical processes affected by climate in the natural environment are scale‐dependent: determining surface‐air‐temperature distribution at a scale of tens to hundreds of metres can facilitate such research, which is currently hampered by the relative dearth of meteorological stations and complex surface temperature characteristics, particularly in mountain areas. Here we discuss both the couplings and mismatch of present climatological data at different scales, ranging from ∼50 m to 100 km, and provide a novel model of the surface‐air‐temperature distribution in topographically heterogeneous regions.First, a comparison of the large‐scale weather station measurements and gridded climate reanalysis (ERA‐40) data is used to define regional climatology in the Swedish sub‐Arctic and obtain the mesoscale temperature lapse rates. Second, combined with temperature measurements obtained from transects set among complex terrain, key microclimatic characteristics of the temperature distribution are identified, showing few temperature inversions when the wind speed exceeds 3 m s−1, while temperature inversions prevail during calm nights. Besides wind, there is a pronounced winter temperature stratification around the large Lake Torneträsk, and variations in topography are found to have a strong influence in shaping the microscale temperature pattern through their effect on solar radiation during summer.A monthly 50‐m scale temperature‐distribution (topoclimate) model is built based on the above findings, and model validation is conducted using further fieldwork measurements from different seasons. We present results of surface‐air‐temperature distribution for the Abisko region, and discuss how these results help reconcile the scale mismatch mentioned above.

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

AbstractPassive and active microwave rain sensors on board Earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscures the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different spaceborne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands, and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean, and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite, and the regional climatology. The most significant result from this study found that each of the satellites incurred negative long-term oceanic retrieval biases of 10%–30%.

Considerations for parameter optimization and sensitivity in climate models

Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.

The Hydrologic Cycle of the La Plata Basin in the WCRP-CMIP3 Multimodel Dataset

Abstract General circulation models (GCMs) forced under different greenhouse gases emission and socioeconomic scenarios are currently the most extended tool throughout the scientific community that is used to infer the future climate on Earth. However, these models still have problems in capturing several aspects of regional climate variability in many parts of the globe. In this paper, the hydrological cycle of the La Plata Basin is simulated using the variable infiltration capacity (VIC) distributed hydrology model and forced with atmospheric data from different GCMs to determine to what extent errors in temperature and precipitation fields impact the hydrology of the basin. The skill assessment is performed in terms of simulated runoff at different closing points. Simulated hydrographs show that all of the GCMs present deficiencies in simulating the regional climatology of southern South America, and this leads to a very poor representation of the hydrological cycle of the main rivers across the basin. Two unbiasing schemes are then proposed as a means of correcting the GCM outputs before forcing the hydrology model, and comparisons between biased and unbiased simulations are also performed. Results indicate that both schemes, though methodologically different, reduce the water cycle simulation bias. Finally, VIC is forced with bias-corrected data from the GCMs for future decades (2030 and 2070) under different socioeconomic scenarios [e.g., the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emissions Scenarios (SRES) scenarios A1B, A2, and B1] to determine the potential changes in streamflow due to climate change for the rest of the present century.

The seasonality of precipitation signals embedded within the North American Drought Atlas

We examine how the seasonality of precipitation signals embedded within the North American Drought Atlas varies across the continent. Instrumental records of average summer (JJA) Palmer Drought Severity Index (PDSI) are characterized by major regional differences in the relative importance of precipitation during summer and winter (DJF). The Atlas, which is based on a network of drought-sensitive tree-ring records, is able to reproduce the main geographic patterns of these biases, but tree-ring reconstructions exaggerate the influence of seasonal precipitation anomalies in the southwestern United States and northern Mexico (towards a stronger winter signal) and western Canada (towards a stronger summer signal). Drought reconstructions from the Southwest and Tex-Mex regions are tuned mainly to winter precipitation and display strong teleconnections to both El Niño and La Niña. In contrast, winter precipitation signals are either weak or absent in drought reconstructions from northwestern North America, and tree-ring estimates of PDSI show a much less robust association with the El Niño-Southern Oscillation. Geographical differences in the relative strength of seasonal precipitation signals are likely due to (i) local factors that influence tree growth but are not incorporated into the PDSI algorithm and (ii) real differences in regional climatology. These seasonal biases must be taken into account when comparing drought reconstructions across North America, when comparing tree-ring PDSI to drought records developed from other proxies or when attempting to use the Drought Atlas to link past droughts to potential forcing mechanisms.

Regional climate of hazardous convective weather through high-resolution dynamical downscaling

We explore the use of high-resolution dynamical downscaling as a means to simulate the regional climatology and variability of hazardous convective-scale weather. Our basic approach differs from a traditional regional climate model application in that it involves a sequence of daily integrations. We use the weather research and forecasting (WRF) model, with global reanalysis data as initial and boundary conditions. Horizontal grid lengths of 4.25 km allow for explicit representation of deep convective storms and hence a compilation of their occurrence statistics over a large portion of the conterminous United States. The resultant 10-year sequence of WRF model integrations yields precipitation that, despite its positive bias, has a diurnal cycle consistent with observations, and otherwise has a realistic geographical distribution. Similarly, the occurrence frequency of short-duration, potentially flooding rainfall compares well to analyses of hourly rain gauge data. Finally, the climatological distribution of hazardous-thunderstorm occurrence is shown to be represented with some degree of skill through a model proxy that relates rotating convective updraft cores to the presence of hail, damaging surface winds, and tornadoes. The results suggest that the proxy occurrences, when coupled with information on the larger-scale atmosphere, could provide guidance on the reliability of trends in the observed occurrences.

Transfer of climate knowledge via a regional climate-change management body to support vulnerability, impact assessments and adaptation measures

We provide an overview of how climate-change-science knowledge transfer is achieved at Ouranos in support of vulnerability and impact assessments and adaptation (V&I&A) activities. Ouranos is a Canadian consortium concerned with regional climatology and adaptation to climate change, launched in 2002 by the Government of Quebec, Hydro-Quebec and the Meteorological Ser- vice of Canada to coordinate climate-change research in Quebec. Focusing on Ouranos' ongoing V&I&A projects in coastal regions, we describe the current knowledge-transfer environment. We also discuss how climate indices and indicators of vulnerability—which are developed by Ouranos follow- ing a 'pressure-state-response' (PSR) framework—form useful knowledge-transfer tools. Two spe- cific case studies exemplify the development of (1) a set of temperature-trend indices for southern Quebec and (2) climate-social indicators for the assessment of risks to public health due to extremely high temperature events. Case Study (1) illustrates how a systematic analysis of climate variability and relevant indices of extremes can be useful for decision makers at regional scales (southern Quebec). Case Study (2) examines the potential, and feasibility, of using a risk-assessment frame- work for regional climate-change studies that focus on impacts and adaptation.

High-resolution simulations of West African climate using regional climate model (RegCM3) with different lateral boundary conditions

To downscale climate change scenarios, long-term regional climatologies employing global model forcing are needed for West Africa. As a first step, this work examines present-day integrations (1981–2000) with a regional climate model (RCM) over West Africa nested in both reanalysis data and output from a coupled atmospheric–ocean general circulation model (AOGCM). Precipitation and temperature from both simulations are compared to the Climate Research Unit observations. Their spatial distributions are shown to be realistic. Annual cycles are considerably correlated. Simulations are also evaluated with respect to the driving large-scale fields. RCM offers some improvements compared to the AOGCM driving field. Evaluation of seasonal precipitation biases reveals that RCM dry biases are highest on June–August around mountains. They are associated to cold biases in temperature which, in turn, are connected to wet biases in precipitation outside orographic zones. Biases brought through AOGCM forcing are relatively low. Despite these errors, the simulations produce encouraging results and show the ability of the AOGCM to drive the RCM for future projections.

Professor T J Chandler

Professor T J Chandler

Characteristics of an East–Asian summer monsoon climatology simulated by the RegCM3

We investigated the characteristics of a 22-year-long regional climatology of the East Asian summer monsoon (EASM) simulated by the Regional Climate Model version 3 (RegCM3). The RegCM3 was forced by the perfect boundary conditions, i.e., the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) reanalysis (R-2), for summer (June–July–August, JJA) during the period of 1982–2003 with a 60 km horizontal resolution. This study stresses the evaluation of the seasonal mean climatology and interannual variations against the R-2 as well as a shorter time-scale variation, such as intraseasonal and diurnal variations over the Korean Peninsula, against the station-observed datasets. The RegCM3 is able to reproduce overall features of the seasonal mean climatology with a wet bias of 0.54 mm day−1 and a certain cold (warm) bias in the lower (upper) troposphere. Interannual variability during the 22 years also shows good agreement with the R-2 reanalysis, both in precipitation and temperature. The capability of the RegCM3 in reproducing intraseasonal variability is reasonable in terms of a 22-year-long climatology. The daily variations of the simulated climatology in surface temperature and in precipitation over Korea are also reasonably reproduced during June and July but are relatively poorly resolved for August. The skill degradation is clearer in the last eight years of 1996–2003 than the first period of 1982–1989, which raises an issue of the RegCM3’s ability to capture the observed decadal change in the mid-1990s. Diurnal variations of surface climate simulated by the model exhibit overestimated (underestimated) amplitudes in precipitation (temperature).

Criteria for wind farm noise: Lmax and Lden

Wind turbine noise limits are based on either the highest sound immission level (Lmax) or several sound immission levels for a series of wind speed classes (Lmax,v). As yet no procedure has been proposed to determine the day-evening-night sound level (Lden) that is now commonly used in the European Union for all noise sources. Wind speed dependent rating wind turbine noise levels Lr,v can be predicted based on climatological data. This has been verified by measurements over a nine month period for a wind farm at a coastal location in the Netherlands. From these measurements also the long term average sound level Lden can be determined. Lden can also be determined from previously published wind speed measurements at an inland location over one year. The procedure shows that for a wind turbine or wind farm the Lden can be derived from Lmax by taking into account the regional climatology.

Land surface response to precipitation events using MODIS and NEXRAD data

We have developed a wavelet‐based information theoretic approach to examine the interaction between precipitation (PPT) forcing events and the land surface response. Combining Next Generation Weather Radar (NEXRAD) PPT with Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation (NDVI) and surface temperature (T s) data over the Missouri Basin in the north‐central USA, we are able to address the spatial and temporal fluctuations surrounding the hydrometeorology of grassland ecosystems. Information theory metrics of entropy and mutual information content are combined with a wavelet multi‐resolution analysis to examine to what extent the observed PPT signal directly determines the spatial distribution of the land surface temperature and vegetation and how this relationship varies with spatial scale. Results indicate that (1) there is a reduction in the temporal variance of the wavelet coefficients as the signal is transferred from the PPT into the surface temperature and finally the vegetation signal, (2) there are significant correlations as a function of spatial resolution between PPT–NDVI and PPT–T s signals which generally increase with spatial resolution, while there is little correlation between the NDVI and T s signals as a function of resolution, and (3) the scale‐wise entropy and the mutual information content of the signals increase for all fields as the spatial resolution increases. This provides a methodology for determining the relative impact of regional climatology and local land–atmosphere interactions as a function of spatial scale.

Rapid techniques for determining annual accumulation applied at Summit, Greenland

AbstractWe have determined accumulation histories by identifying annual-layer horizons in records obtained by three independent methods: (1) glaciochemical analysis on a core, (2) density profiling in the borehole from which the core was taken, using the neutron-probe (NP) technique, and (3) borehole optical stratigraphy (BOS), again in the same borehole. We also used three different techniques for determining density to convert annual-layer thickness to accumulation: (1) gravimetric measurements on core samples, (2) measurement of density using NP and (3) a simple empirical model based on regional climatology. The result is nine different accumulation time series, three of which are completely independent. The chemical-analysis- and NP-derived accumulation time series are correlated, and the ∼70 year means are in agreement. The BOS-derived accumulation ∼70 year mean is slightly lower, probably due to a combination of the empirical density model’s underestimate of the density profile and the misidentification of sub-annual events in the shallow part of the borehole as annual horizons.

Regional climatology of particulate carbonaceous substances in the northern area of the east Asian Pacific rim

A year‐round observation of atmospheric aerosols and their associated species was conducted from March 2001 to May 2002 on Rishiri Island in the northern area of the east Asian Pacific rim region. Asian outflows brought continental air masses to this area during the period from the autumn to the spring although marine air masses from the high‐latitudinal ocean often broke into this area during the midwinter. In contrast, marine air masses were predominant over this area in the summer. Particulate elemental carbon (EC) would be mainly transported with biomass smoke particles, and seasonal variation in its concentrations was well correlated with the air mass alternation, showing higher concentrations during the period from the autumn to the spring with some decreases in the midwinter. The concentrations of particulate organic carbon (OC) showed a similar seasonal trend with those of the particulate EC, but relatively high concentrations were found in the summer due to photochemical secondary productions. The particulate OC that is vaporized by heating at higher temperatures (OCHT) would be mainly carried with the biomass smoke, and the particulate OC that is vaporized at lower temperatures (OCLT) would be mainly caused by secondary production processes. Summer enhancements of the secondary species, such as OC and nss‐SO42–, caused decreases in the ratio of EC/PM2.5, which would contribute to the high single scattering albedo of fine aerosols in the summer. Aerosols in the Asian outflows in this area are relatively “black” in the winter, although the aerosol mass loading increases in the spring.

Analysis of Weak Wind Stable Conditions from the Observations of the Land Surface Processes Experiment at Anand in India

The observations in weak wind stable conditions are scarce. The present study examines the observations from the Land Surface Processes Experiment (LASPEX) conducted at Anand, (Gujarat, India) during the year 1997–1998 to study the characteristics of surface layer under weak wind stable conditions. The observed surface fluxes are compared with those computed using Monin-Obukhov (M-O) similarity theory. The upper air observations and regional climatology are used to justify the persistence of weak wind conditions at Anand. The frequency of occurrence of weak wind stable conditions is observed to be around 67%. In 86% of the cases under weak wind conditions, bulk Richardson number (R iB ) is found to be larger than 0.2. The magnitude of surface fluxes computed from M-O similarity theory is shown to be smaller in comparison to those based on the observations in weak wind stable conditions. Surface fluxes computed using the empirical relations for the eddy diffusivities and drag and heat exchange coefficients are found to be comparable with those based on M-O similarity theory however these fluxes are under-predicted in comparison to the observations. The traditional M-O similarity theory is not able to simulate the observed fluxes well in weak wind stable conditions at Anand.

Developing a high-resolution climatology for the Central California coastal region

This work presents a procedure for developing a high-resolution, regional climatology estimate, named RClimo, off the coast of central California. This high-resolution climatology may provide an alternative way to initialize numerical nowcast/forecast exercises in coastal regions. The methodology includes two primary steps: (1) averaging available data on a high-resolution grid and (2) objective interpolating the resulting average profiles onto a regular grid. The first step involves the computation of averages over density layers in the vertical and allowing for data gaps in the horizontal if data are unavailable at a high resolution. The OA in the second step uses anisotropic correlation length scales derived from the data themselves and an averaging radius to preserve the scales and variability of the synoptic fields. The dataset used to compute this climatology includes the archived CalCOFI dataset, the Autonomous Ocean Sampling Network (AOSN) 2003 experiments near Monterey Bay, and many other previously undocumented profiles from various sources. As part of the climatology product, associated uncertainty is also generated through density averaging and employing the Gauss–Markov minimum error variance during Objective Analysis. The final climatology estimate is hence subject to greater error for larger mapping-grid size or lower data density, suggesting uncertainties that vary in space. The maximum value of the resulting error distribution for the RClimo estimate from the 50-km bins is less than 11% of the temperature estimate and 1% of the salinity estimate, whereas those for the 20-km-bin RClimo are 11% and 0.9%, respectively. We have conducted comparisons between the RClimo and 1/4° Levitus climatology fields via numerical simulations initialized with each field. Simulations were performed using Regional Ocean Modeling System (ROMS) and for the month of August, a peak period of upwelling-favorable wind, with and without the atmospheric forcing. The RClimo simulations produced reasonably realistic features like filaments, fronts, meanders, eddies, alongshore currents (∼50 cm s −1) and inshore undercurrents (∼15 cm s −1) under typical upwelling-favorable winds. In contrast, the Levitus simulations showed insignificant evolution over most of the domain, with or without winds. In the future, one could apply this methodology for other months and seasons in Monterey Bay, as well as develop similar high-resolution climatology for other coastal regions.