Korea Meteorological Administration

Abstract ERA‐Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF). The ERA‐Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA‐40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA‐Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA‐40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

The MODIS cloud mask uses several cloud detection tests to indicate a level of confidence that the MODIS is observing clear skies. It will be produced globally at single‐pixel resolution; the algorithm uses as many as 14 of the MODIS 36 spectral bands to maximize reliable cloud detection and to mitigate past difficulties experienced by sensors with coarser spatial resolution or fewer spectral bands. The MODIS cloud mask is ancillary input to MODIS land, ocean, and atmosphere science algorithms to suggest processing options. The MODIS cloud mask algorithm will operate in near real time in a limited computer processing and storage facility with simple easy‐to‐follow algorithm paths. The MODIS cloud mask algorithm identifies several conceptual domains according to surface type and solar illumination, including land, water, snow/ice, desert, and coast for both day and night. Once a pixel has been assigned to a particular domain (defining an algorithm path), a series of threshold tests attempts to detect the presence of clouds in the instrument field of view. Each cloud detection test returns a confidence level that the pixel is clear ranging in value from 1 (high) to zero (low). There are several types of tests, where detection of different cloud conditions relies on different tests. Tests capable of detecting similar cloud conditions are grouped together. While these groups are arranged so that independence between them is maximized, few, if any, spectral tests are completely independent. The minimum confidence from all tests within a group is taken to be representative of that group. These confidences indicate absence of particular cloud types. The product of all the group confidences is used to determine the confidence of finding clear‐sky conditions. This paper outlines the MODIS cloud masking algorithm. While no present sensor has all of the spectral bands necessary for testing the complete MODIS cloud mask, initial validation of some of the individual cloud tests is presented using existing remote sensing data sets.

Abstract Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

Abstract. The scientific understanding of the Earth's climate system, including the central question of how the climate system is likely to respond to human-induced perturbations, is comprehensively captured in GCMs and Earth System Models (ESM). Diagnosing the simulated climate response, and comparing responses across different models, is crucially dependent on transparent assumptions of how the GCM/ESM has been driven – especially because the implementation can involve subjective decisions and may differ between modelling groups performing the same experiment. This paper outlines the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document the prescribed greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the HadGEM2-ES historical and future experiments following the Representative Concentration Pathways. In addition, we provide details of how HadGEM2-ES ensemble members were initialised from the control run and how the palaeoclimate and AMIP experiments, as well as the "emission-driven" RCP experiments were performed.

Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

Abstract El Niño and Southern Oscillation (ENSO) is the most prominent year‐to‐year climate fluctuation on Earth, alternating between anomalously warm (El Niño) and cold (La Niña) sea surface temperature (SST) conditions in the tropical Pacific. ENSO exerts its impacts on remote regions of the globe through atmospheric teleconnections, affecting extreme weather events worldwide. However, these teleconnections are inherently nonlinear and sensitive to ENSO SST anomaly patterns and amplitudes. In addition, teleconnections are modulated by variability in the oceanic and atmopsheric mean state outside the tropics and by land and sea ice extent. The character of ENSO as well as the ocean mean state have changed since the 1990s, which might be due to either natural variability or anthropogenic forcing, or their combined influences. This has resulted in changes in ENSO atmospheric teleconnections in terms of precipitation and temperature in various parts of the globe. In addition, changes in ENSO teleconnection patterns have affected their predictability and the statistics of extreme events. However, the short observational record does not allow us to clearly distinguish which changes are robust and which are not. Climate models suggest that ENSO teleconnections will change because the mean atmospheric circulation will change due to anthropogenic forcing in the 21st century, which is independent of whether ENSO properties change or not. However, future ENSO teleconnection changes do not currently show strong intermodel agreement from region to region, highlighting the importance of identifying factors that affect uncertainty in future model projections.

The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.

Ensemble forecasting is increasingly accepted as a powerful tool to improve early warnings for high-impact weather. Recently, ensembles combining forecasts from different systems have attracted a considerable level of interest. The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Globa l Ensemble (TIGGE) project, a prominent contribution to THORPEX, has been initiated to enable advanced research and demonstration of the multimodel ensemble concept and to pave the way toward operational implementation of such a system at the international level. The objectives of TIGGE are 1) to facilitate closer cooperation between the academic and operational meteorological communities by expanding the availability of operational products for research, and 2) to facilitate exploring the concept and benefits of multimodel probabilistic weather forecasts, with a particular focus on high-impact weather prediction. Ten operational weather forecasting centers producing daily global ensemble forecasts to 1–2 weeks ahead have agreed to deliver in near–real time a selection of forecast data to the TIGGE data archives at the China Meteorological Agency, the European Centre for Medium-Range Weather Forecasts, and the National Center for Atmospheric Research. The volume of data accumulated daily is 245 GB (1.6 million global fields). This is offered to the scientific community as a new resource for research and education. The TIGGE data policy is to make each forecast accessible via the Internet 48 h after it was initially issued by each originating center. Quicker access can also be granted for field experiments or projects of particular interest to the World Weather Research Programme and THORPEX. A few examples of initial results based on TIGGE data are discussed in this paper, and the case is made for additional research in several directions.

Abstract. The COordinated Regional Downscaling EXperiment (CORDEX) is a diagnostic model intercomparison project (MIP) in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global climate model (GCM) output to produce downscaled projected changes in regional climates and assess sources of uncertainties in the projections, all of which can potentially be distilled into climate change information for vulnerability, impacts and adaptation studies. CORDEX Flagship Pilot Studies advance regional downscaling by targeting one or more of the CORDEX Regional Challenges. A CORDEX-CORE framework is planned that will produce a baseline set of homogeneous high-resolution, downscaled projections for regions worldwide. In CMIP6, CORDEX coordinates with ScenarioMIP and is structured to allow cross comparisons with HighResMIP and interaction with the CMIP6 VIACS Advisory Board.

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980-2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models' MME for the period of 1981-2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Nin o 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the

Abstract Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system 1 . Remote-sensing estimates to quantify carbon losses from global forests 2–5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced 6 and satellite-derived approaches 2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea 2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

In support of the first Tropospheric Ozone Assessment Report (TOAR) a relational database of global surface ozone observations has been developed and populated with hourly measurement data and enhanced metadata. A comprehensive suite of ozone data products including standard statistics, health and vegetation impact metrics, and trend information, are made available through a common data portal and a web interface. These data form the basis of the TOAR analyses focusing on human health, vegetation, and climate relevant ozone issues, which are part of this special feature. Cooperation among many data centers and individual researchers worldwide made it possible to build the world’s largest collection of in-situ hourly surface ozone data covering the period from 1970 to 2015. By combining the data from almost 10,000 measurement sites around the world with global metadata information, new analyses of surface ozone have become possible, such as the first globally consistent characterisations of measurement sites as either urban or rural/remote. Exploitation of these global metadata allows for new insights into the global distribution, and seasonal and long-term changes of tropospheric ozone and they enable TOAR to perform the first, globally consistent analysis of present-day ozone concentrations and recent ozone changes with relevance to health, agriculture, and climate. Considerable effort was made to harmonize and synthesize data formats and metadata information from various networks and individual data submissions. Extensive quality control was applied to identify questionable and erroneous data, including changes in apparent instrument offsets or calibrations. Such data were excluded from TOAR data products. Limitations of a posteriori data quality assurance are discussed. As a result of the work presented here, global coverage of surface ozone data for scientific analysis has been significantly extended. Yet, large gaps remain in the surface observation network both in terms of regions without monitoring, and in terms of regions that have monitoring programs but no public access to the data archive. Therefore future improvements to the database will require not only improved data harmonization, but also expanded data sharing and increased monitoring in data-sparse regions.

[1] Top-down constraints on global sulfur dioxide (SO2) emissions are inferred through inverse modeling using SO2 column observations from two satellite instruments (SCIAMACHY and OMI). We first evaluated the SO2 column observations with surface SO2 measurements by applying local scaling factors from a global chemical transport model (GEOS-Chem) to SO2 columns retrieved from the satellite instruments. The resulting annual mean surface SO2 mixing ratios for 2006 exhibit a significant spatial correlation (r = 0.86, slope = 0.91 for SCIAMACHY and r = 0.80, slope = 0.79 for OMI) with coincident in situ measurements from monitoring networks throughout the United States and Canada. We evaluate the GEOS-Chem simulation of the SO2 lifetime with that inferred from in situ measurements to verify the applicability of GEOS-Chem for inversion of SO2 columns to emissions. The seasonal mean SO2 lifetime calculated with the GEOS-Chem model over the eastern United States is 13 h in summer and 48 h in winter, compared to lifetimes inferred from in situ measurements of 19 ± 7 h in summer and 58 ± 20 h in winter. We apply SO2 columns from SCIAMACHY and OMI to derive a top-down anthropogenic SO2 emission inventory over land by using the local GEOS-Chem relationship between SO2 columns and emissions. There is little seasonal variation in the top-down emissions (<15%) over most major industrial regions providing some confidence in the method. Our global estimate for annual land surface anthropogenic SO2 emissions (52.4 Tg S yr−1 from SCIAMACHY and 49.9 Tg S yr−1 from OMI) closely agrees with the bottom-up emissions (54.6 Tg S yr−1) in the GEOS-Chem model and exhibits consistency in global distributions with the bottom-up emissions (r = 0.78 for SCIAMACHY, and r = 0.77 for OMI). However, there are significant regional differences.

Abstract In this study, spatial and temporal patterns of changes in extreme events of temperature and precipitation at 143 weather stations in ten Asia‐Pacific Network (APN) countries and their associations with changes in climate means are examined for the 1955–2007 period. Averaged over the APN region, annual frequency of cool nights (days) has decreased by 6.4 days/decade (3.3 days/decade), whereas the frequency of warm nights (days) has increased by 5.4 days/decade (3.9 days/decade). The change rates in the annual frequency of warm nights (days) over the last 20 years (1988–2007) have exceeded those over the full 1955–2007 period by a factor of 1.8 (3.4). Seasonally, the frequencies of summer warm nights and days are changing more rapidly per unit change in mean temperatures than the corresponding frequencies for cool nights and days. However, normalization of the extreme and mean series shows that the rate of changes in extreme temperature events are generally less than that of mean temperatures, except for winter cold nights which are changing as rapidly as the winter mean minimum temperature. These results indicate that there have been seasonally and diurnally asymmetric changes in extreme temperature events relative to recent increases in temperature means in the APN region. There are no systematic, regional trends over the study period in total precipitation, or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at fewer than 30% of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 70% of all weather stations, forming strongly coherent spatial patterns. Copyright © 2009 Royal Meteorological Society

One-point correlation analysis on upper-level meridional wind identified the existence of a teleconnection pattern in July, which emerges from North Africa to East Asia along the westerly jet in the middle latitudes. We examined the spatial and temporal structures of this teleconnection pattern, and found the unique characteristics rather different from the patterns in other elements such as geopotential height, streamfunction and vorticity. We also investigated the relationship between this teleconnection and precipitation, and suggested that the teleconnectionis a possible linkage of the EASM to the Indian monsoon, and even to subtropical heating anomalies over Atlantic.

Abstract The spatial and temporal structure of the urban heat island in Seoul, Korea, is investigated using near-surface temperature data measured at 31 automatic weather stations (AWSs) in the Seoul metropolitan area for the 1-yr period from March 2001 to February 2002. The urban heat island in Seoul deviates considerably from an idealized, concentric heat island structure, mainly because of the location of the main commercial and industrial sectors and the local topography. Relatively warm regions extend in the east–west direction and relatively cold regions are located near the northern and southern mountains. Several warm cores are observed whose intensity, size, and location are found to vary seasonally and diurnally. Similar to previous studies, the urban heat island in Seoul is stronger in the nighttime than in the daytime and decreases with increasing wind speed and cloud cover, but it is least developed in summer. The average maximum urban heat island intensity is 2.2°C over the 1-yr period and it is 3.4°C at 0300 local standard time (LST) and 0.6°C at 1500 LST. The reversed urban heat island is occasionally observed in the afternoon, but its intensity is very weak. An empirical orthogonal function (EOF) analysis is performed to find the dominant modes of variability in the Seoul urban heat island. In the analysis using temperature data that are averaged for each hour of the 1-yr period, the first EOF explains 80.6% of the total variance and is a major diurnal mode. The second EOF, whose horizontal structure is positive in the eastern part of Seoul and is negative in the western part, explains 16.0% of the total variance. This mode is related to the land use type and the diurnal pattern of anthropogenic heat release. In the analysis using temperature data at 0300 LST, the leading four modes explain 72.4% of the total variance. The first EOF reflects that the weakest urban heat island intensity is in summer. It is found that the urban heat island in Seoul is stronger on weekdays than weekends.

Abstract We present an estimate of net ecosystem exchange (NEE) of CO 2 in Europe for the years 2001–2007. It is derived with a data assimilation that uses a large set of atmospheric CO 2 mole fraction observations (∼70 000) to guide relatively simple descriptions of terrestrial and oceanic net exchange, while fossil fuel and fire emissions are prescribed. Weekly terrestrial sources and sinks are optimized (i.e., a flux inversion) for a set of 18 large ecosystems across Europe in which prescribed climate, weather, and surface characteristics introduce finer scale gradients. We find that the terrestrial biosphere in Europe absorbed a net average of −165 Tg C yr −1 over the period considered. This uptake is predominantly in non‐EU countries, and is found in the northern coniferous (−94 Tg C yr −1 ) and mixed forests (−30 Tg C yr −1 ) as well as the forest/field complexes of eastern Europe (−85 Tg C yr −1 ). An optimistic uncertainty estimate derived using three biosphere models suggests the uptake to be in a range of −122 to −258 Tg C yr −1 , while a more conservative estimate derived from the a‐posteriori covariance estimates is −165±437 Tg C yr −1 . Note, however, that uncertainties are hard to estimate given the nature of the system and are likely to be significantly larger than this. Interannual variability in NEE includes a reduction in uptake due to the 2003 drought followed by 3 years of more than average uptake. The largest anomaly of NEE occurred in 2005 concurrent with increased seasonal cycles of observed CO 2 . We speculate these changes to result from the strong negative phase of the North Atlantic Oscillation in 2005 that lead to favorable summer growth conditions, and altered horizontal and vertical mixing in the atmosphere. All our results are available through http://www.carbontracker.eu

An intercomparison study involving eight dust emission/transport models over Asia (DMIP) has been completed. Participating dust models utilize a variety of dust emission schemes, horizontal and vertical resolutions, numerical methods, and different meteorological models. Two huge dust episodes occurred in spring 2002 and were used for the DMIP study. Meteorological parameters, dust emission flux and dust concentration (diameter &lt; 20 μm) are compared within the same domain on the basis of PM and NIES lidar measurements. We found that modeled dust concentrations between the 25% and 75% percentiles generally agreed with the PM observations. The model results correctly captured the major dust onset and cessation timing at each observation site. However, the maximum concentration of each model was 2–4 times different. Dust emission fluxes from the Taklimakan Desert and Mongolia differ immensely among the models, indicating that the dust source allocation scheme over these regions differs greatly among the various modeling groups. This suggests the measurements of dust flux and accurate updated land use information are important to improve the models over these regions. The dust vertical concentration profile at Beijing, China, and Nagasaki, Japan, has a large scatter (more than two times different) among the models. For Beijing, the scaled dust profile has a quite similar vertical profile and shows relatively good agreement with the lidar extinction profile. However, for Nagasaki, the scaled dust profiles do not agree. These results indicate that modeling of dust transport and removal processes between China and Japan is another important issue in improving dust modeling.

Abstract Trends (1961–2003) in daily maximum and minimum temperatures, extremes and variance were found to be spatially coherent across the Asia–Pacific region. The majority of stations exhibited significant trends: increases in mean maximum and mean minimum temperature, decreases in cold nights and cool days, and increases in warm nights. No station showed a significant increase in cold days or cold nights, but a few sites showed significant decreases in hot days and warm nights. Significant decreases were observed in both maximum and minimum temperature standard deviation in China, Korea and some stations in Japan (probably reflecting urbanization effects), but also for some Thailand and coastal Australian sites. The South Pacific convergence zone (SPCZ) region between Fiji and the Solomon Islands showed a significant increase in maximum temperature variability. Correlations between mean temperature and the frequency of extreme temperatures were strongest in the tropical Pacific Ocean from French Polynesia to Papua New Guinea, Malaysia, the Philippines, Thailand and southern Japan. Correlations were weaker at continental or higher latitude locations, which may partly reflect urbanization. For non‐urban stations, the dominant distribution change for both maximum and minimum temperature involved a change in the mean, impacting on one or both extremes, with no change in standard deviation. This occurred from French Polynesia to Papua New Guinea (except for maximum temperature changes near the SPCZ), in Malaysia, the Philippines, and several outlying Japanese islands. For urbanized stations the dominant change was a change in the mean and variance, impacting on one or both extremes. This result was particularly evident for minimum temperature. The results presented here, for non‐urban tropical and maritime locations in the Asia–Pacific region, support the hypothesis that changes in mean temperature may be used to predict changes in extreme temperatures. At urbanized or higher latitude locations, changes in variance should be incorporated. Copyright © 2005 Royal Meteorological Society.

Abstract Understanding the long‐term performance of global satellite leaf area index ( LAI ) products is important for global change research. However, few effort has been devoted to evaluating the long‐term time‐series consistencies of LAI products. This study compared four long‐term LAI products ( GLASS , GLOBMAP , LAI 3g, and TCDR ) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products ( GEOV 1, MERIS , MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long‐term LAI products. In general, there were marked discrepancies between the four long‐term LAI products. During the pre‐ MODIS period (1982–1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS &gt; LAI 3g&gt; TCDR &gt; GLOBMAP . The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP . During the overlap period (2003–2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI 3g an increasing trend. GEOV 1, MERIS , and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI 3g. During both periods, the R 2 of detrended anomalies between the four long‐term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long‐term LAI products were considerably different over the two periods, and the differences followed the order GLASS &gt; LAI 3g&gt; TCDR &gt; GLOBMAP . Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long‐term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long‐term LAI products.