Global Observational Data Research Articles

Impact of bias‐corrected reanalysis‐derived lateral boundary conditions on WRF simulations

AbstractLateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA‐Interim (ERA‐I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near‐surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA‐I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias‐corrected WRF simulations are, however, found to be marginally better than mean only bias‐corrected WRF simulations and raw ERA‐I reanalysis‐driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Role of internal variability in recent decadal to multidecadal tropical Pacific climate changes

AbstractWhile the Earth's surface has considerably warmed over the past two decades, the tropical Pacific has featured a cooling of sea surface temperatures in its eastern and central parts, which went along with an unprecedented strengthening of the equatorial trade winds, the surface component of the Pacific Walker Circulation (PWC). Previous studies show that this decadal trend in the trade winds is generally beyond the range of decadal trends simulated by climate models when forced by historical radiative forcing. There is still a debate on the origin of and the potential role that internal variability may have played in the recent decadal surface wind trend. Using a number of long control (unforced) integrations of global climate models and several observational data sets, we address the question as to whether the recent decadal to multidecadal trends are robustly classified as an unusual event or the persistent response to external forcing. The observed trends in the tropical Pacific surface climate are still within the range of the long‐term internal variability spanned by the models but represent an extreme realization of this variability. Thus, the recent observed decadal trends in the tropical Pacific, though highly unusual, could be of natural origin. We note that the long‐term trends in the selected PWC indices exhibit a large observational uncertainty, even hindering definitive statements about the sign of the trends.

Collaborative Regression on Aerosol Optical Thickness from Heterogeneous Remote Sensing Data

Previous studies of aerosol optical thickness (AOT) estimations were generally based on observations from a single satellite sensor. Due to the limited observations from one instrument, the observations yielded AOT estimations with a system bias. In this paper, we combined two heterogeneous data sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), together and proposed collaborative regression models to achieve more accurate AOT estimations than a single sensor does. These two independent remote sensors in the A-train satellite constellation both provide global AOT retrievals and they scan the same location on the Earth surface within a two-minute interval. However, the two remote sensors have different design principles respectively and their heterogeneous observation data streams pose challenges for information fusion. In our study, we proposed two types of heterogeneous collaborative regression approaches. One type of collaborative regression approach fuses information in a feature level. The other type of collaborative approach combines information in a model level. In our study, in each level, we apply a linear regression collaboration model and a neural network collaboration model. The proposed approaches are evaluated based on global observation data from MODIS and CALIOP during April 2, 2009 and April 1, 2011. The encouraging experimental results show that the regression approach collaborating in a model level achieves significantly more accurate AOT estimations than the results from the collaborative regression approach in a feature level. It also obtains significantly superior results to the deterministic AOT retrievals from any single satellite sensor.

Using climate models to estimate the quality of global observational data sets.

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.

The strong El Niño of 2015/16 and its dominant impacts on global and China's climate

The oceanic and atmospheric conditions and the related climate impacts of the 2015/16 ENSO cycle were analyzed, based on the latest global climate observational data, especially that of China. The results show that this strong El Nino event fully established in spring 2015 and has been rapidly developing into one of the three strongest El Nino episodes in recorded history. Meanwhile, it is also expected to be the longest event recorded, attributable to the stable maintenance of the abnormally warm conditions in the equatorial Pacific Ocean since spring 2014. Owing to the impacts of this strong event, along with climate warming background, the global surface temperature and the surface air temperature over Chinese mainland reached record highs in 2015. Disastrous weather in various places worldwide have occurred in association with this severe El Nino episode, and summer precipitation has reduced significantly in North China, especially over the bend of the Yellow River, central Inner Mongolia, and the coastal areas surrounding Bohai Bay. Serious drought has occurred in some of the above areas. The El Nino episode reached its peak strength during November-December 2015, when a lower-troposphere anomalous anticyclonic circulation prevailed over the Philippines, bringing about abnormal southerlies and substantially increased precipitation in southeastern China. At the same time, a negative phase of the Eurasia-Pacific teleconnection pattern dominated over the mid-high latitudes, which suppressed northerly winds in North China. These two factors together resulted in high concentrations of fine particulate matter (PM2.5) and frequent haze weather in this region. Currently, this strong El Nino is weakening very rapidly, but its impact on climate will continue in the coming months in some regions, especially in China.

Vertical heat flux in the ocean: Estimates from observations and from a coupled general circulation model

AbstractThe net heat uptake by the ocean in a changing climate involves small imbalances between the advective and diffusive processes that transport heat vertically. Generally, it is necessary to rely on global climate models to study these processes in detail. In the present study, it is shown that a key component of the vertical heat flux, namely that associated with the large‐scale mean vertical circulation, can be diagnosed over extra‐tropical regions from global observational data sets. This component is estimated based on the vertical velocity obtained from the geostrophic vorticity balance, combined with estimates of absolute geostrophic flow. Results are compared with the output of a non‐eddy resolving, coupled atmosphere‐ocean general circulation model. Reasonable agreement is found in the latitudinal distribution of the vertical heat flux, as well as in the area‐integrated flux below about 250 m depth. The correspondence with the coupled model deteriorates sharply at depths shallower than 250 m due to the omission of equatorial regions from the calculation. The vertical heat flux due to the mean circulation is found to be dominated globally by the downward contribution from the Southern Hemisphere, in particular the Southern Ocean. This is driven by the Ekman vertical velocity which induces an upward transport of seawater that is cold relative to the horizontal average at a given depth. The results indicate that the dominant characteristics of the vertical transport of heat due to the mean circulation can be inferred from simple linear vorticity dynamics over much of the ocean.

Observationally based analysis of land–atmosphere coupling

Abstract. The temporal variance of soil moisture, vegetation and evapotranspiration over land has been recognized to be strongly connected to the temporal variance of precipitation. However, the feedbacks and couplings between these variables are still not well understood and quantified. Furthermore, soil moisture and vegetation processes are associated with a memory and therefore they may have important implications for predictability. In this study we apply a generalized linear method, specifically designed to assess the reciprocal forcing between connected fields, to the latest available observational data sets of global precipitation, evapotranspiration, vegetation and soil moisture content. For the first time a long global observational data set is used to investigate the spatial and temporal land variability and to characterize the relationships and feedbacks between land and precipitation. The variables considered show a significant coupling among each other. The analysis of the response of precipitation to soil moisture evidences a robust coupling between these two variables. In particular, the first two modes of variability in the precipitation forced by soil moisture appear to have a strong link with volcanic eruptions and El Niño–Southern Oscillation (ENSO) cycles, respectively, and these links are modulated by the effects of evapotranspiration and vegetation. It is suggested that vegetation state and soil moisture provide a biophysical memory of ENSO and major volcanic eruptions, revealed through delayed feedbacks on rainfall patterns. The third mode of variability reveals a trend very similar to the trend of the inter-hemispheric contrast in sea surface temperature (SST) and appears to be connected to greening/browning trends of vegetation over the last three decades.

The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set

Abstract. Here, the authors describe the construction of a forcing data set for land surface models (including both physical and biogeochemical models; LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison study, called GTMIP, which is a part of the Japanese-funded Arctic Climate Change Research Project. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries (i.e. without missing data), four observational sites across the pan-Arctic region (Fairbanks, Tiksi, Yakutsk, and Kevo) were selected to construct a blended data set using both global reanalysis and observational data. Marked improvements were found in the diurnal cycles of surface air temperature and humidity, wind speed, and precipitation. The data sets and participation in GTMIP are open to the scientific community (doi:10.17592/001.2015093001).

Future ocean hypercapnia driven by anthropogenic amplification of the natural CO2 cycle.

High carbon dioxide (CO2) concentrations in sea-water (ocean hypercapnia) can induce neurological, physiological and behavioural deficiencies in marine animals. Prediction of the onset and evolution of hypercapnia in the ocean requires a good understanding of annual variations in oceanic CO2 concentration, but there is a lack of relevant global observational data. Here we identify global ocean patterns of monthly variability in carbon concentration using observations that allow us to examine the evolution of surface-ocean CO2 levels over the entire annual cycle under increasing atmospheric CO2 concentrations. We predict that the present-day amplitude of the natural oscillations in oceanic CO2 concentration will be amplified by up to tenfold in some regions by 2100, if atmospheric CO2 concentrations continue to rise throughout this century (according to the RCP8.5 scenario of the Intergovernmental Panel on Climate Change). The findings from our data are broadly consistent with projections from Earth system climate models. Our predicted amplification of the annual CO2 cycle displays distinct global patterns that may expose major fisheries in the Southern, Pacific and North Atlantic oceans to hypercapnia many decades earlier than is expected from average atmospheric CO2 concentrations. We suggest that these ocean 'CO2 hotspots' evolve as a combination of the strong seasonal dynamics of CO2 concentration and the long-term effective storage of anthropogenic CO2 in the oceans that lowers the buffer capacity in these regions, causing a nonlinear amplification of CO2 concentration over the annual cycle. The onset of ocean hypercapnia (when the partial pressure of CO2 in sea-water exceeds 1,000 micro-atmospheres) is forecast for atmospheric CO2 concentrations that exceed 650 parts per million, with hypercapnia expected in up to half the surface ocean by 2100, assuming a high-emissions scenario (RCP8.5). Such extensive ocean hypercapnia has detrimental implications for fisheries during the twenty-first century.

The star formation history of low-mass disk galaxies: A case study of NGC 300

Since NGC300 is a bulge-less, isolated low-mass galaxy and has not experienced radial migration during its evolution history, it can be treated as an ideal laboratory to test simple galactic chemical evolution models. By assuming its disk forms gradually from continuous accretion of primordial gas and including the gas-outflow process, we construct a simple chemical evolution model for NGC300 to build a bridge between its SFH and its observed data, especially the present-day radial profiles and global observed properties (e.g., cold gas mass, star-formation rate and metallicity). By means of comparing the model predictions with the corresponding observations, we adopt the classical $\chi^{2}$ methodology to find out the best combination of free parameters $a$, $b$ and $b_{\rm out}$. Our results show that, by assuming an inside-out formation scenario and an appropriate outflow rate, our model reproduces well most of the present-day observational values, not only the radial profiles but also the global observational data for the NGC300 disk. Our results suggest that NGC300 may experience a rapid growth of its disk. Through comparing the best-fitting model predicted SFH of NGC300 with that of M33, we find that the mean stellar age of NGC300 is older than that of M33 and there is a lack of primordial gas infall onto the disk of NGC300 recently. Our results also imply that the local environment may paly a key role in the secular evolution of NGC300.

In-situ and Remote Sensing Networks for Environmental Monitoring and Global Assessment of Leptospirosis Outbreaks

Leptospirosis is a disease that affects human population and can claim many victims with large outbreaks associated with natural disasters. This work focuses on the technological aspects for inexpensive climate monitoring techniques based on ground and satellite sensors for obtaining information prior to disease outbreaks in under-developed regions and on water-quality sensors that can lead to radical changes in our ability to detect and abate this disease. The remote deployment of such sensors in areas where outbreaks can occur can help in enhancingin real-time the spatial and temporal resolution of information and allows unattended operation that will be particularly useful for monitoring under extreme climate events. Such types of monitoring advancements, when coupled with regular geographical, population and habitat monitoring can assess the hazards and risks to local population prior to a disease outbreak. Then in the eventual aftermath, it can assist in identification of affected geographical locations where abatement solutions will be required, and eventually in the assessment of the effectiveness of control measures. This work explores recent releases of open global observation data and a range of in-situ environmental monitoring tools of increasing complexity for measuring several parameters andfor detecting contaminants and pathogens that were previously irresolvable due to the high degree of complexityinthe diagnosis of this disease.

Contribution of precipitation and reference evapotranspiration to drought indices under different climates

Summary In this study we analyzed the sensitivity of four drought indices to precipitation (P) and reference evapotranspiration (ETo) inputs. The four drought indices are the Palmer Drought Severity Index (PDSI), the Reconnaissance Drought Index (RDI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Palmer Drought Index (SPDI). The analysis uses long-term simulated series with varying averages and variances, as well as global observational data to assess the sensitivity to real climatic conditions in different regions of the World. The results show differences in the sensitivity to ETo and P among the four drought indices. The PDSI shows the lowest sensitivity to variation in their climate inputs, probably as a consequence of the standardization procedure of soil water budget anomalies. The RDI is only sensitive to the variance but not to the average of P and ETo. The SPEI shows the largest sensitivity to ETo variation, with clear geographic patterns mainly controlled by aridity. The low sensitivity of the PDSI to ETo makes the PDSI perhaps less apt as the suitable drought index in applications in which the changes in ETo are most relevant. On the contrary, the SPEI shows equal sensitivity to P and ETo. It works as a perfect supply and demand system modulated by the average and standard deviation of each series and combines the sensitivity of the series to changes in magnitude and variance. Our results are a robust assessment of the sensitivity of drought indices to P and ETo variation, and provide advice on the use of drought indices to detect climate change impacts on drought severity under a wide variety of climatic conditions.

Ten ways remote sensing can contribute to conservation.

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Exploring the Potential of SRTM Topography and Radar Altimetry to Support Flood Propagation Modeling: Danube Case Study

Flood inundation modeling is one of the essential steps in flood hazard mapping. However, the desirable input and calibration data for model building and evaluation are not sufficient or unavailable in many rivers and floodplains of the world. A potential opportunity to fill this gap is offered nowadays by global earth observation data, which can be obtained freely (or at low cost), such as the shuttle radar topography mission (SRTM) and radar altimetry. However, the actual usefulness of these data is still poorly investigated. This study attempts to assess the value of SRTM topography and radar altimetry in supporting flood-level predictions in data-poor areas. To this end, a hydraulic model of a 150-km reach of the Danube River was built by using SRTM topography as input data and radar altimetry of the 2006 flood event as calibration data. The model was then used to simulate the 2007 flood event and evaluated against water levels measured in four stream gauge stations. Model evaluation allows the investigation of the usefulness and limitations of SRTM topography and radar altimetry in supporting hydraulic modeling of floods.

The change of global terrestrial ecosystem net primary productivity (NPP) and its response to climate change in CMIP5

Using global terrestrial ecosystem observation and proxy data for net primary productivity (NPP), leaf area index (LAI), and climate data, we compared simulated NPP, LAI, and major climatic factors and explored the relationship between their variations in historical scenarios of ten Coupled Model Intercomparison Project (CMIP5) models. The results showed that global spatial patterns of the simulated terrestrial ecosystem and climate are consistent with proxy data, but the values have some differences for each model. Based on statistical analysis, the simulated climatic factors were found to be better than terrestrial ecosystem NPP and LAI, and the multi-model ensemble (MME) results were better than every single model. For the terrestrial ecosystem, air temperature (Ta) was found to be the major affecting factor, followed by precipitation, meaning the terrestrial ecosystem NPP and LAI are more related to Ta than precipitation. Meanwhile, surface downwelling shortwave radiation (Rsds) was found to inhibit the terrestrial ecosystem in almost all regions of the world. Between 1976 and 2005, precipitation had a slight increasing trend, Ta an obvious increasing trend, and Rsds a slight decreasing trend. The changes of precipitation, air temperature, and Rsds were favorable for the terrestrial ecosystem and for plant growth. Therefore, LAI and NPP showed an obvious increasing temporal trend, and the terrestrial ecosystem showed a positive response to climate change. All the model results showed NPP had an increasing temporal trend in the past 150 years, which also indicated that the terrestrial ecosystem has shown a positive response to climate change in that time period. In terms of the global average, the simulated NPP varied from 21.5 to 69.3 Pg C year−1, and the MME NPP is about 50.6, which was almost consistent with the International Geosphere Biosphere Program (IGBP) NPP result of 55.1 and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP results of 60.5 Pg C year−1.

Communication Duration and Missed Passes among Terminals and Satellites for Search and Rescue Services

Aims: NOAAs (National Oceanic and Atmospheric Administration) -LEO environmental satellites provide continuous coverage of Earth, supplying high -resolution global meteorological, oceanic and space observation data. These satellites are part of the international Search and Rescue Satellite Aided Tracking (SARSAT) system. SARSAT is designed to provide distress alert and location data in order to assist on search and rescue operation. SARSAT system detects and locates distress beacons (406MHz) activated at distress location. System calculates the distress event location using Doppler processing techniques. Each satellite pass transmits information about distress location. Passes with too short communication duration, are considered as m

Development of Global Hourly 0.5° Land Surface Air Temperature Datasets

Abstract Land surface air temperature (SAT) is one of the most important variables in weather and climate studies, and its diurnal cycle is also needed for a variety of applications. Global long-term hourly SAT observational data, however, do not exist. While such hourly products could be obtained from global reanalyses, they are found to be unrealistic in representing the SAT diurnal cycle. Global hourly 0.5° SAT datasets are developed here based on four reanalysis products [Modern-Era Retrospective Analysis for Research and Applications (MERRA for 1979–2009), 40-yr ECMWF Re-Analysis (ERA-40 for 1958–2001), ECMWF Interim Re-Analysis (ERA-Interim for 1979–2009), and NCEP–NCAR reanalysis for 1948–2009)] and the Climate Research Unit Time Series version 3.10 (CRU TS3.10) for 1948–2009. The three-step adjustments include the spatial downscaling to 0.5° grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP–NCAR reanalysis) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the bias correction in both monthly-mean maximum (Tmax) and minimum (Tmin) SAT using the CRU data. The final products have exactly the same monthly Tmax and Tmin as the CRU data, and perform well in comparison with in situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in the final products can be quantified by their differences in the true monthly mean (using 24-hourly values) and the monthly averaged diurnal cycle.

Elevation Variation with Low Earth Orbiting Search and Rescue Satellites for the Station Implemented in Kosovo

NOAA's (National Oceanic and Atmospheric Administration) - LEO (Low Earth Orbit) environmental satellites provide continuous coverage of Earth, supplying high-resolution global meteorological, oceanic and space observation data what are too important in aerospace and maritime. In addition, these satellites are part of the international Search and Rescue Satellite Aided Tracking (SARSAT) system. SARSAT is a satellite system designed to provide distress alert and location data in order to assist on search and rescue operation. The position of the satellite within its orbit considered from the ground station point of view is defined by Azimuth and Elevation angles. There is a direct relationship between communication duration and maximal elevation. Higher maximal elevation provides longer communication between the satellite and a Local User Terminal (LUT). Longer communication provides more Doppler events providing higher accuracy on location determination. Passes with too short communication duration, consequently with no data provided are considered as missed passes. Under assumption that the LUT is implemented in Kosovo, it is simulated the variation of elevation for different orbits for a period of one month. Simulation about elevation variations, among SARSAT satellites and local user terminal dedicated for search and rescue services is provided through this paper.

Future changes in low precipitation patterns projected by the super‐high‐resolution MRI‐AGCM3.1S and CMIP3 AOGCMs

AbstractThis study assesses future changes in low precipitation patterns over land around the globe under the Special Report on Emissions Scenarios A1B scenario. We use global precipitation data sets derived by the super‐high‐resolution Atmospheric General Circulation Model of the Japan Meteorological Agency and Meteorological Research Institute (MRI‐AGCM3.1S) and Atmospheric Ocean coupled General Circulation Models compiled in the phase 3 of the Coupled Model Intercomparison Project (CMIP3 AOGCMs). The low precipitation patterns refer to the temporal and spatial distribution of annual minimum of accumulated precipitation over a given time interval using occurrence probability and observation windows in unfixed seasons. As for the low precipitation patterns, this study assesses low precipitation quantiles in six probability levels approximated by the Weibull distribution using annual minima of monthly accumulated precipitation over 1‐, 3‐, 6‐ and 12‐month time intervals. Also, low precipitation occurrence season were assessed from the centroid of the frequency distribution of the ending months with annual minima of the low precipitation over the four time intervals. The model capability of reproducing current low precipitation patterns was examined in reference to the global precipitation observation data set of VASClimO, from which MRI‐AGCM3.1S showed the best performance among all models. The MRI‐AGCM3.1S projections, as well as the multi‐model ensemble mean calculated over 16 GCMs, indicate that 0.1 probability low precipitation quantiles in 3‐ and 6‐month intervals would decrease by 10 to 50% in Mexico, southern Brazil, southern Argentina, Mediterranean area and southern Africa with high model consistency. The projected shifts in low precipitation occurrence seasons were of concern; however, they show a considerable degree of uncertainty with low model consistency. Copyright © 2013 John Wiley & Sons, Ltd.

Hotspots of the sensitivity of the land surface hydrological cycle to climate change

Due to the shortage of the global observational data of the terrestrial hydrological variables, the understanding of how surface hydrological processes respond to climate change is still limited. In this study, the Community Land Model (CLM4.0) with high resolution atmospheric forcing data is selected to simulate the global surface hydrological quantities during the period 1948–2006 and to investigate the spatial features of these quantities in response to climate change at the regional scales. The sensitivities of evaporation and runoff with respect to the dominant climate change factors (e.g. temperature and precipitation) derived from the concept of climate elasticity are introduced. Results show that evaporation has a declining trend with a rate of 0.7 mm per decade, while runoff shows a weak increasing trend of 0.15 mm per decade over the global land surface. Analyses of the hotspots in the hydrological cycle indicate that the spatial distributions for evaporation and runoff are similar over many areas in central Asia, Australia, and southern South America, but differ largely in high latitudes. It is also found that, the evaporation hotspots in arid regions are mainly associated with the changes in precipitation. Our sensitive analysis suggests that the hydrological quantities show a rather complicated spatial dependency of response of the water cycle to the different climate factors (temperature and precipitation).