Climatological Datasets Research Articles

SPARC Data Initiative: A comparison of ozone climatologies from international satellite limb sounders

AbstractA comprehensive quality assessment of the ozone products from 18 limb‐viewing satellite instruments is provided by means of a detailed intercomparison. The ozone climatologies in form of monthly zonal mean time series covering the upper troposphere to lower mesosphere are obtained from LIMS, SAGE I/II/III, UARS‐MLS, HALOE, POAM II/III, SMR, OSIRIS, MIPAS, GOMOS, SCIAMACHY, ACE‐FTS, ACE‐MAESTRO, Aura‐MLS, HIRDLS, and SMILES within 1978–2010. The intercomparisons focus on mean biases of annual zonal mean fields, interannual variability, and seasonal cycles. Additionally, the physical consistency of the data is tested through diagnostics of the quasi‐biennial oscillation and Antarctic ozone hole. The comprehensive evaluations reveal that the uncertainty in our knowledge of the atmospheric ozone mean state is smallest in the tropical and midlatitude middle stratosphere with a 1σ multi‐instrument spread of less than ±5%. While the overall agreement among the climatological data sets is very good for large parts of the stratosphere, individual discrepancies have been identified, including unrealistic month‐to‐month fluctuations, large biases in particular atmospheric regions, or inconsistencies in the seasonal cycle. Notable differences between the data sets exist in the tropical lower stratosphere (with a spread of ±30%) and at high latitudes (±15%). In particular, large relative differences are identified in the Antarctic during the time of the ozone hole, with a spread between the monthly zonal mean fields of ±50%. The evaluations provide guidance on what data sets are the most reliable for applications such as studies of ozone variability, model‐measurement comparisons, detection of long‐term trends, and data‐merging activities.

SMILES zonal and diurnal variation climatology of stratospheric and mesospheric trace gasses: O3, HCl, HNO3, ClO, BrO, HOCl, HO2, and temperature

We present a climatology of the diurnal variation of short‐lived atmospheric compounds, such as ClO, BrO, HO2, and HOCl, as well as longer‐lived species: O3, the hydrogen chloride isotopes H35Cl and H37Cl, and HNO3. Measurements were taken by the Superconducting Submillimeter‐wave Limb‐Emission Sounder (SMILES). This spectrally resolving radiometer, with very low observation noise and altitude range from the lower stratosphere to the lower thermosphere (20–100km), was measuring vertical profiles of absorption spectra along a non‐sun‐synchronous orbit, thus observing at all local times. We used the retrieved volume mixing ratio profiles to compile climatologies that are a function of pressure, a horizontal coordinate (latitude or equivalent latitude), and a temporal coordinate (solar zenith angle or local solar time). The main product presented are climatologies with a high resolution of the temporal coordinate (diurnal variation climatologies). In addition, we provide climatologies with a high resolution of the horizontal coordinate (zonal climatologies).The diurnal variation climatologies are based on data periods of 2 months and the zonal climatologies on monthly data periods. Consideration of the SMILES time‐space sampling patterns with respect to the averaging coordinates is a key issue for climatology creation, especially in case of diurnal variation climatologies. Biases induced by inhomogeneous sampling are minimized by carefully choosing the size of averaging bins. The sampling biases of the diurnal variation climatology of ClO and BrO are investigated in a comparison of homogeneously sampled model data versus SMILES‐sampled model data from the stratospheric Lagrangian chemistry and transport model Alfred Wegener Institute Lagrangian Chemisrty/Transport System. In most cases, the relative sampling error is in the range of 0–20%. The strongest impact of sampling biases is found where the species' temporal gradients are strongest (mostly at sunrise and sunset), with a relative error of 60–100%. The SMILES climatology data sets are available via the SMILES data distribution home page.

Sensitivity of the modeled present-day Greenland Ice Sheet to climatic forcing and spin-up methods and its influence on future sea level projections

[1] The accuracy of the modeled present-day Greenland Ice Sheet (GrIS) is crucial for future projections of GrIS changes. In this study, we present a detailed evaluation of the modeled present-day GrIS sensitivity to different present-day climatology data sets and spin-up methods and further assess the influence of the modeled present-day GrIS on future sea level projections. Our study demonstrates that with present-day climatology data sets derived from the Regional Atmospheric Climate Model (RACMO2), the modeled ice volume, area, and elevation of the GrIS agree substantially better with observations compared to simulations with present-day climatology data sets from temperature parameterizations and ERA-interim reanalysis. With transient spin-up, the simulated rates of ice elevation changes for the 1993–2010 period are closer to observations than those with steady state spin-up. These results indicate that the RACMO2 forcing and transient spin-up may be preferable for use in the ice sheet model. Moreover, our results reveal that the present-day climatology data sets and spin-up methods affect future sea level projections. Using transient spin-up, the ice sheet model estimates a range of 26 mm in projected sea level rise by 2098 under the A1B emissions scenario, due to different present-day climatology data sets. Compared to the results with transient spin-up, the estimated sea level rise by 2098 is reduced by 5–21 mm with steady state spin-up. This discrepancy is attributed mainly to differences in ice thickness and ice velocity between the modeled present-day GrIS with transient and steady state spin-ups and the effect of paleoclimatic changes with transient spin-up.

Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

Abstract One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classical k-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993–2011).

A high-resolution modeling study of the Western Iberian Margin mean and seasonal upper ocean circulation

The mean seasonal hydrography and circulation of the Western Iberian Margin (WIM) are studied by means of a high-resolution configuration of the Regional Oceanic Modeling System. A comparison of 5-year model averages for January and July with climatological datasets shows a general good agreement in the reproduction of the mean water mass properties and hydrographic distribution. We find that there is a prevailing tendency for slope poleward flow at about 80–100 km offshore at all latitudes from the surface to 1,500 m with strong vertical coupling. This northward flow, which is mainly along slope and amounts up to 8–10 cm s−1, exhibits several mean flow recirculation regions on its way and evidences of an offshore pathway of poleward flow. Transports at different zonal sections further confirm the poleward flow tendency with two peaks of poleward transport in summer (3–10 Sv) and winter (2–7 Sv). The transport time series emphasize the seasonal character of the alongshore circulation and the interannual intrinsic variability of the circulation, since the forcing fields are climatological. As a conceptual essay with the purpose of assessing the Mediterranean Water flow influence on the WIM mean circulation, a second model configuration is setup, where the Mediterranean outflow into the study domain is removed. We find that there is an attenuation of the mesoscale field, but the slope poleward flow intensifies and remains as a mean dynamical feature closer to the upper slope.

Atmospheric column-averaged mole fractions of carbon dioxide at 53 aircraft measurement sites

Abstract. Atmospheric column-averaged mole fractions of carbon dioxide (XCO2) at 53 locations around the world were derived from aircraft measurements covering the altitude range of about 1–10 km. We used CO2 vertical profile measurements from three major carbon cycle programs, a global climatological data set of air number density profiles and tropopause height for calculating XCO2 for the period of 2007–2009. Vertical profiles of the CO2 mixing ratio are complemented by tall tower data up to 400 m from the earth's surface and by simulated profiles in the stratosphere from a chemistry-transport model. The amplitude of the seasonal cycle of calculated XCO2 values shows clear latitudinal dependence, and the amplitude decreases from about 10 ppm at high latitudes in the Northern Hemisphere to at most 2 ppm in the tropics and the Southern Hemisphere. The uncertainties of XCO2 were estimated from assumptions about CO2 profiles for each flight. Typically, uncertainties were less than 1 ppm; thus, this data set is within the level of uncertainty needed for primary validation of XCO2 measurements by the Greenhouse gases Observing SATellite (GOSAT) and by future satellite missions for monitoring greenhouse gases.

Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard

Abstract. A consistent meteorological dataset of the Arctic site Ny-Ålesund (11.9° E, 78.9° N) spanning the 18 yr-period 1 August 1993 to 31 July 2011 is presented. Instrumentation and data handling of temperature, humidity, wind and pressure measurements are described in detail. Monthly mean values are shown for all years to illustrate the interannual variability of the different parameters. Climatological mean values are given for temperature, humidity and pressure. From the climatological dataset, we also present the time series of annual mean temperature and humidity, revealing a temperature increase of +1.35 K per decade and an increase in water vapor mixing ratio of +0.22 g kg−1 per decade for the given time period, respectively. With the continuation of the presented measurements, the Ny-Ålesund high resolution time series will provide a reliable source to monitor Arctic change and retrieve trends in the future. The relevant data are provided in high temporal resolution as averages over 5 (1) min before (after) 14 July 1998, respectively, placed on the PANGAEA repository (doi:10.1594/PANGAEA.793046). While 6 hourly synoptic observations in Ny-Ålesund by the Norwegian Meteorological Institute reach back to 1974 (Førland et al., 2011), the meteorological data presented here cover a shorter time period, but their high temporal resolution will be of value for atmospheric process studies on shorter time scales.

Pacific Storms Climatology Products (PSCP): Understanding Extreme Events

E xtreme events: the f loods that displace us from our homes, the high waves that wash out coastal roads, or the toppling of trees and power poles from a passing storm. For locations around the Pacific Basin, where remote island chains sit perilously close to sea level and where rainfall is the primary source of water, questions arise concerning the return frequency and duration of such events, and whether or not they are getting more extreme. Understanding the long-term variability and change in coastal climate extremes has grown in public awareness given the potentially severe impacts related to sea-level rise coupled with coastal storms. To reduce vulnerability to the economic, social, and environmental risks associated with these phenomena, decision makers in coastal communities need timely access to accurate and contextually relevant information that affords them an opportunity to plan and respond accordingly. To address this need, the Pacific Storms Climatology Products (PSCP) project—or Pacific Storms— was established under the direction of the NOAA National Climatic Data Center (NCDC). Pacific Storms is focused on improving our understanding of patterns and trends in storm frequency and intensity—“storminess”—within the Pacific Basin. Pacific Storms is exploring how the climate-related processes that govern extreme storm events are expressed within and between three thematic areas: strong winds, heavy rains, and high seas. Theme-specif ic data integration and product development teams were formed to conduct analyses and create a broad suite of derived data products, which are publicly available online (www.pacificstormsclimatology.org). These teams included representatives from NCDC, as well as NOAA’s Center for Operational Products and Services (CO-OPS), Coastal Services Center (CSC), and National Weather Service (NWS), and the University of Hawaii, University of Alaska, University of Guam, and Oregon State University. Sources of information include NOAA’s Integrated Surface Hourly (ISH) mean sea-level pressure and wind speed data, the Global Historical Climate Network-Daily (GHCN-D) precipitation dataset, the National Water Level Observing Network (NWLON) tide gauge records, the University of Hawaii Sea Level Center (UHSLC) Joint Archive for Sea Level research quality dataset and Global Sea Level Observing System (GLOSS)/Climate Variability and Predictability (CLIVAR) “fast delivery” sea level dataset, the National Data Buoy Center (NDBC) wave buoy records, the U.S. Army Corps of Engineers’ Coastal Data Information (CDIP) buoy data, and other data sources. The data analysis and product development framework and guidelines outlined below are innovative in a number of ways. First, they focus on extreme events, and integrate data and products across a range of storm-related phenomena. Furthermore, they also paint a comprehensive picture of changes and variation in extreme-event magnitude and frequency for a mix of theme-specific parameters on seasonal, annual, and interannual time frames. The resulting extremes climatology datasets are unique, as are some of the specific products. Finally, success of the project is fundamentally tied to the collaborative efforts of the data integration and product development teams.

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.

Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model

The leaf area index (LAI), defined as the one-sided green leaf area per unit ground area, is used in many numerical weather prediction (NWP) models as an indicator of the vegetation development state, which is of paramount importance to characterize land evaporation, photosynthesis, and carbon-uptake processes. LAI is often simply represented by lookup tables, dependent on the vegetation type and seasons. However, global LAI datasets derived from remote sensing observations have more recently become available. These products are based on sensors such as the Advanced Very High Resolution Radiometer (AVHRR) or the Moderate Resolution Imaging Spectroradiometer (MODIS), onboard polar orbiting satellites that can cover the entire globe within typically 3 days and with a spatial resolution of the order of 1 km. We examine the meteorological impact of satellite-derived LAI products on near-surface air temperature and humidity, which comes both from the stomatal transpiration of leaves and from the intercepted water on the surface of leaves, re-evaporating into the atmosphere. Two distinct monthly LAI climatology datasets derived respectively from AVHRR and MODIS sensors are tested. A set of forecasts and data assimilation experiments with the integrated forecasting system of the European Centre for Medium-range Weather Forecasts is performed with the monthly LAI climatology datasets as opposed to a vegetation-dependent constant LAI. The monthly LAI is shown to improve the forecasts of near-surface (screen-level) air temperature and relative humidity through its effect on evapotranspiration, with the largest impact obtained over needleleaf forests, crops, and grassland. At longer time-scales, the introduction of the monthly LAI is shown to have a positive impact on the model climate particularly during the boreal spring, where the LAI climatology has a large seasonal cycle.

남한 강수량 분포 추정을 위한 PRISM 매개변수 및 수치표고모형 최적화

생태환경모델링, 수문모델링, 기후변화 영향평가 등 다양한 분야에서 정규 격자 형태의 기후자료에 대한 요구가 증가하고 있다. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model)은 다양한 격자형태의 기후자료 생산방법 중 고지대의 강수량 추정에 유용한 방법이다. 그러나 국내에서는 이 모델의 매개변수 및 모델에 사용되는 수치표고모형의 공간해상도 최적화에 대한 논의가 충분하지 않았다. 이에 본 연구에서는 PRISM을 개발하였다. 그리고 SCE-UA(Shuffled Complex Evolution-University of Arizona) 기법을 이용하여 2000-2005년 1km 공간해상도의 남한 연평균 강수 격자자료를 생산하는 데 필요한 PRISM 매개변수 최적값 및 DEM의 적정 공간해상도를 추정하였다. 아울러 매개변수와 수치표고모형에 대한 PRISM의 민감도 분석을 수행하였다. 그 결과 PRISM 모델에서 관측소 최대 탐색반경(67km)과 최소반경(31km), 지형고도-강수량의 선형회귀식 산정에 필요한 최소 관측소 개수(4개), 수치표고모형의 적정 공간해상도(<TEX>$1{\times}1km$</TEX>) 등을 결정하였다. 그리고 PRISM 모의 결과가 수치표고모형의 공간해상도에 매우 민감하다는 것을 확인하였다. 본 연구결과는 PRISM 기법을 국내에 적용할 때 정확도를 향상시키는데 기여할 것으로 기대된다. The demand for a climatological dataset with a regular spaced grid is increasing in diverse fields such as ecological and hydrological modeling as well as regional climate impact studies. PRISM(Precipitation-Elevation Regressions on Independent Slopes Model) is a useful method to estimate high-altitude precipitation. However, it is not well discussed over the optimization of PRISM parameters and DEM(Digital Elevation Model) resolution in South Korea. This study developed the PRISM and then optimized parameters of the model and DEM resolution for producing a gridded annual average precipitation data of South Korea with 1km spatial resolution during the period 2000-2005. SCE-UA (Shuffled Complex Evolution-University of Arizona) method employed for the optimization. In addition, sensitivity analysis investigates the change in the model output with respect to the parameter and the DEM spatial resolution variations. The study result shows that maximum radius within which station search will be conducted is 67km. Minimum radius within which all stations are included is 31km. Minimum number of stations required for cell precipitation and elevation regression calculation is four. Optimizing DEM resolution is <TEX>$1{\times}1km$</TEX>. This study also shows that the PRISM output very sensitive to DEM spatial resolution variations. This study contributes to improving the accuracy of PRISM technique as it applies to South Korea.

Technical Note: A trace gas climatology derived from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) data set

Abstract. The Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) aboard the Canadian satellite SCISAT (launched in August 2003) was designed to investigate the composition of the upper troposphere, stratosphere, and mesosphere. ACE-FTS utilizes solar occultation to measure temperature and pressure as well as vertical profiles of over thirty chemical species including O3, H2O, CH4, N2O, CO, NO, NO2, N2O5, HNO3, HCl, ClONO2, CCl3F, CCl2F2, and HF. Global coverage for each species is obtained approximately over a three month period and measurements are made with a vertical resolution of typically 3–4 km. A quality-controlled climatology has been created for each of these 14 baseline species, where individual profiles are averaged over the period of February 2004 to February 2009. Measurements used are from the ACE-FTS version 2.2 data set including updates for O3 and N2O5. The climatological fields are provided on a monthly and three-monthly basis (DJF, MAM, JJA, SON) at 5 degree latitude and equivalent latitude spacing and on 28 pressure surfaces (26 of which are defined by the Stratospheric Processes And their Role in Climate (SPARC) Chemistry-Climate Model Validation Activity). The ACE-FTS climatological data set is available through the ACE website.

Automatic detection of atmospheric boundary layer height using ceilometer backscatter data assisted by a boundary layer model

AbstractThis work tackles the problem of the automated detection of the atmospheric boundary layer (BL) height h, from aerosol lidar/ceilometer observations. A new method, the Bayesian selective method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in a statistically optimal way different sources of information. Firstly, atmospheric stratification boundaries are located from discontinuities in the ceilometer backscattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneous physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice.The BSM algorithm has been tested for 4 months of continuous ceilometer measurements collected during the BASE:ALFA project, and is shown to realistically diagnose the BL depth evolution in many different weather conditions. A standard one‐dimensional processing of the ceilometer signal without the a priori support of the dynamical and climatological BL models often fails to correctly detect h, with the greatest inaccuracies occurring at night‐time when residual layers can generate very strong signals, which are then classified by an automated application of the gradient or of the wavelet analysis as the most probable BL height. The BSM approach instead carries information on the low climatological probability to find elevated BL depths at night and penalizes the selection of these points. Moreover, this method is able to correctly convey information along the temporal dimension, thus filling data gaps using earlier and subsequent ceilometer information for the retrieval. Copyright © 2011 Royal Meteorological Society

Comparison of Gridded and Measured Rainfall Data for Basin-scale Hydrological Studies

Global gridded climatological (GGC) datasets, including precipitation and temperature, are becoming more and more precise, accessible, and common, but the utility of these datasets and their limits for hydrological research are still not well determined. In this paper, we compare the performance of two hydrological models that are identical in structure but built with two different inputs: rainfall from rain gauge stations and from a GGC dataset. The objective is to evaluate the utility of gridded datasets in water resource availability studies mainly for hydroelectric and agricultural purposes. The Andean basin of the Laja River, located in south-central Chile, was chosen for this study. It was based on an 18-yr simulation, and it was concluded that i) with gridded climatological datasets in a monthly water balance model, it is possible to reproduce the behavior of an Andean basin with good goodness-of-fit, but with worse results than when using inputs from rain gauges; ii) the amount of rainfall in gridded datasets in the Andean area of the Laja basin is underestimated and damped, an effect which is transferred to the simulated flows; and iii) regarding the main activities in the Laja basin, global gridded datasets are useful for hydrological studies with agricultural purposes prior to a treatment that considers the orographic effect. On the other hand, these datasets are useless for hydroelectric purposes due to the large underestimation of peak flows obtained during the rainy season.

Bivariate colour maps for visualizing climate data

AbstractThe increasing availability of gridded, high‐resolution, multivariate climatological data sets calls for innovative approaches to visualize inter‐variable relations. In this study, we present a methodology, based on properties of common colour schemes, to plot two variables in a single colour map by using a two‐dimensional colour legend for both sequential and diverging data. This is especially suited for climate data as the spatial distribution of the relation between different variables is often as important as the distribution of variables individually. Two example applications are given to illustrate the use of the method: one that shows the global distribution of climate based on observed temperature and relative humidity, and the other showing the distribution of recent changes in observed temperature and precipitation over Europe. A flexible and easy‐to‐implement method is provided to construct different colour legends for sequential and diverging data. Copyright © 2010 Royal Meteorological Society

Impact of episodic vertical fluxes on sea surface pCO 2

Episodic events like hurricanes, storms and frontal- and eddy-driven upwelling can alter the partial pressure of CO(2) (pCO(2)) at the sea surface by entraining subsurface waters into the surface mixed layer (ML) of the ocean. Since pCO(2) is a function of total dissolved inorganic carbon (DIC), temperature (T), salinity and alkalinity, it responds to the combined impacts of physical, chemical and biological changes. Here, we present an analytical framework for assessing the relative magnitude and sign in the short-term perturbation of surface pCO(2) arising from vertical mixing events. Using global, monthly, climatological datasets, we assess the individual, as well as integrated, contribution of various properties to surface pCO(2) in response to episodic mixing. The response depends on the relative vertical gradients of properties beneath the ML. Many areas of the ocean exhibit very little sensitivity to mixing owing to the compensatory effects of DIC and T on pCO(2), whereas others, such as the eastern upwelling margins, have the potential to generate large positive/negative anomalies in surface pCO(2). The response varies seasonally and spatially and becomes more intense in subtropical and subpolar regions during summer. Regions showing a greater pCO(2) response to vertical mixing are likely to exhibit higher spatial variability in surface pCO(2) on time scales of days.

고해상도 기후자료를 이용한 우리나라의 논 관개요구량 예측

기후변화가 우리나라의 논 관개요구량에 미치는 영향을 분석하였다. 기상연구소의 SRES A2 시나리오에 대한 고해상도 (<TEX>$27{\times}27\;km$</TEX>) 기후 자료와 기준년도 (1971~2000)의 관측 기상자료를 이용하였다. 기상연구소가 전지구모형 ECHO-G 예측자료를 MM5 모형으로 역학적으로 상세화한 결과를 이용하였다. 논 관개요구량의 공간적인 변화를 분석하기 위하여 GIS 기법을 이용하였다. 연구결과는 벼 생육기간의 평균기온은 기준년도에 비해 <TEX>$1.5^{\circ}C$</TEX> (2020s), <TEX>$3.3^{\circ}C$</TEX> (2050s) 및 <TEX>$5.3^{\circ}C$</TEX> (2080s) 상승할 것으로 예측되었다. 벼 생육기간의 강우량은 0.1% (2020s), 4.9% (2050s) 및 19.3% (2080s) 증가할 것으로 예측되었다. 영농 형태와 논 면적이 변하지 않는다고 가정하면 우리나라 논의 총 관개용수량은 2.8% (2020s), 4.9% (2050s) 및 4.5% (2080s) 증가할 것으로 예측되었다. 본 연구의 결과는 총 관개용수량이 다소 감소할 것으로 예측한 다른 연구결과와는 상반된 결과를 나타내었으며, 그 주원인은 사용된 GCM에 따른 기후 예측치의 차이에서 온 것으로 판단된다. 관개용수량의 시공간적 변동성이 큰 것으로 나타났으며 이는 앞으로 관개계획과 물 관리에서 고려되어야 할 것이다. The impacts of climate change on paddy irrigation water demands in Korea have been analyzed. High-resolution (<TEX>$27{\times}27\;km$</TEX>) climate data for the SRES A2 scenario produced by the Korean Meteorological Research Institute (METRI) and the observed baseline climatology dataset were used. The outputs from the ECHO-G GCM model were dynamically downscaled using the MM5 regional model by the METRI. The Geographic information system (GIS) was used to produce maps showing the spatial changes in irrigation water requirements for rice paddies. The results showed that the growing season mean temperature for future scenarios was projected to increase by <TEX>$1.5^{\circ}C$</TEX> (2020s), <TEX>$3.3^{\circ}C$</TEX> (2050s) and <TEX>$5.3^{\circ}C$</TEX> (2080s) as compared with the baseline value (1971~2000). The growing season rainfall for future scenarios was projected to increase by 0.1% (2020s), 4.9% (2050s) and 19.3% (2080s). Assuming cropping area and farming practices remain unchanged, the total volumetric irrigation demand was projected to increase by 2.8% (2020s), 4.9% (2050s) and 4.5% (2080s). These projections are contrary to the previous study that used HadCM3 outputs and projected decreasing irrigation demand. The main reason for this discrepancy is the difference with the projected climate of the GCMs used. The temporal and spatial variations were large and should be considered in the irrigation water resource planning and management in the future.

Mesoscale vortices in the Ligurian Sea and their effect on coastal upwelling processes

We study numerically the dynamics of intense anticyclonic eddies in the Ligurian Sea (NW Mediterranean Sea). To this end, we use the Regional Ocean Modeling System (ROMS) with a resolution of 3 km for a domain covering the whole Ligurian Sea, with an embedded child grid covering the northwestern part of Ligurian Sea at resolution 1 km. The model is forced with daily boundary conditions obtained from the MFS dataset for the year 2006 at the open lateral boundaries. Surface heat and evapotranspiration fluxes are provided by the monthly climatological dataset COADS at 1/2° spatial resolution. For wind forcing, we consider two configurations. In the first setting, the model is forced by the COADS climatological monthly mean wind stresses; in a second configuration, the model is forced by the daily mean wind stresses provided by a mesoscale meteorological model for the area of interest in the year 2006. The latter setting shows the formation of intense anticyclonic eddy structures in the coastal area, generated by the variable winds and by the interaction of transient currents with bottom and coastal topography (in the NW part of the Ligurian Sea). Comparison of model output with satellite SST data shows definite agreement between numerical results and observations. Analysis of the simulation results over the whole year 2006 and of SST satellite images in 2006 and 2007 indicates that coastal anticyclonic eddies are of common occurrence in the Ligurian Sea, with several events per year, mainly concentrated in autumn and winter. The eddies are characterized by a complex pattern of intense vertical velocities and induce strong, long-lasting coastal upwelling events. For this reason, anticyclonic vortices in the coastal area can generate bursts of nutrient input in the euphotic layer and contribute to the fertilization of the Ligurian Sea, with potentially important effects on the dynamics of phyto- and zooplankton.

Socioeconomic patterns in climate data

Summary To generate a climate data set, temperature data collected at the Earth’s surface must be adjusted to remove non-climatic effects such as urbanization and measurement discontinuities. Some studies have shown that the post-1980 spatial pattern of temperature trends over land in prominent climate data sets is strongly correlated with the spatial pattern of socioeconomic development, implying that the adjustments are inadequate, leaving a residual warm bias. This evidence has been disputed on three grounds: spatial autocorrelation of the temperature field undermines significance of test results; counterfactual experiments using model-generated data suggest such correlations have an innocuous interpretation; and different satellite covariates yield unstable results. Somewhat surprisingly, these claims have not been put into a coherent framework for the purpose of statistical testing. We combine economic and climatological data sets from various teams with trend estimates from global climate models and we use spatial regressions to test the competing hypotheses. Overall we find that the evidence for contamination of climatic data is robust across numerous data sets, it is not undermined by controlling for spatial autocorrelation, and the patterns are not explained by climate models. Consequently we conclude that important data products used for the analysis of climate change over global land surfaces may be contaminated with socioeconomic patterns related to urbanization and other socioeconomic processes. Research Supported by Social Sciences and Humanities Research Council of Canada Grant Number 430002

Seasonal and interannual variability of surface heat and freshwater fluxes in the Mediterranean Sea: budgets and exchange through the Strait of Gibraltar

AbstractSeveral NCEP climatological datasets have been combined to analyse the seasonal and interannual variations of the heat and water budgets in the Mediterranean Sea, and compare the long‐term means with direct measurements in the Strait of Gibraltar. The seasonal cycle of the net heat is positive (toward the ocean) between March and September with maximum in June and negative the rest of the year with minimum in December. Although subject to inherent uncertainty, we obtain a practically neutral budget of 0.7 Wm−2 in a yearly basis. The net heat budget is positive for the western Mediterranean (∼12 Wm−2) and negative for the eastern Mediterranean (∼− 6.4 Wm−2) mainly due to the high latent heat losses of this basin. The evaporation minus precipitation (E–P) freshwater deficit has a seasonal cycle with a range of variation about 600 mm·y−1, maximum in August–September and minimum in May. The long‐term mean of the basin‐averaged deficit is 680 ± 70 mm·y−1 but it is almost 70% greater in the eastern Mediterranean due to higher E and lower P in this basin. Combining the climatological values with in situ measurements in Espartel sill, a mean inflow through the Strait of Gibraltar of 0.82 ± 0.05 Sv is obtained and a salinity ratio Sin/Sout = 0.956. A heat advection Qa = 3.2 ± 1.5 Wm−2 through the Strait of Gibraltar has been obtained that, combined with the long‐term averaged surface heat flux, implies that the net heat content of the Mediterranean Sea would have increased in the last decades. Copyright © 2010 Royal Meteorological Society