Search

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

[1] This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El NiQ no Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes.

Small particles in the atmosphere, called "atmospheric aerosol" have a direct effect on Earth climate through scattering and absorbing sunlight, and also an indirect effect by changing the properties of clouds, as they interact with solar radiation as well. Aerosol typically stays in the atmosphere for several days, and can be transported long distances, affecting air quality, visibility, and human health not only near the source, but also far downwind. Smoke from vegetation fires is one of the main sources of atmospheric aerosol; other sources include anthropogenic pollution, dust, and sea salt. Chemistry transport models (CTMs) are among the major tools for studying the atmospheric and climate effects of aerosol. Due to the considerable variation of aerosol concentrations and properties on many temporal and spatial scales, and the complexity of the processes involved, the uncertainties in aerosol effects on climate are large, as is featured in the latest report of Intergovernmental Panel on Climate Change (IPCC) in 2007. Reducing this uncertainty in the models is very important both for predicting future climate scenarios and for regional air quality forecasting and mitigation. During vegetation fires, also called biomass burning (BB) events, complex mixture of gases and particles is emitted. The amount of BB emissions is usually estimated taking into account the intensity and size of the fire and the properties of burning vegetation. These estimates are input into CTMs to simulate BB aerosol. Unfortunately, due to large variability of fire and vegetation properties, the quantity of BB emissions is very difficult to estimate and BB emission inventories provide numbers that can differ by up to the order of magnitude in some regions. Larger uncertainties in data input make uncertainties in model output larger as well. A powerful way to narrow the range of possible model estimates is to compare model output to observations. We use satellite observations of aerosol properties, specifically aerosol optical depth, which is directly proportional to the amount of aerosol in the atmosphere, and compare it to the model output. Assuming the model represents aerosol transport and particle properties correctly, the amount of BB emissions determines the simulated aerosol optical depth. In this study, we explore the regional performance of 13 commonly used emission estimates. These are each input to global Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. We then evaluate how well each emission estimate reproduces the smoke aerosol optical depth measured by the MODIS instrument. We compared GOCART-simulate aerosol optical depth with that measured from the satellite for 124 fire cases around the world during 2006 and 2007. We summarize the regional performance of each emission inventory and discuss reasons for their differences by considering the assumptions made during their development. We also show that because stronger wind disperses smoke plumes more readily, in cases with stronger wind, a larger increase in emission amount is needed to increase aerosol optical depth. In quiet, low-wind-speed environments, BB emissions produce a more significant increase in aerosol optical depth, other things being equal. Using the region-specific, quantitative relationships derived in our paper, together with the wind speed obtained from another source for a given fire case, we can constrain the amount of emission required in the model to reproduce the observations. The results of this paper are useful to the developers of BB emission inventories, as they show the strengths and weaknesses of individual emission inventories in different regions of the globe, and also for modelers who use these inventories and wish to improve their model results.

The computer codes that calculate the energy budget of solar and thermal radiation in Global Climate Models (GCMs), our most advanced tools for predicting climate change, have to be computationally efficient in order to not impose undue computational burden to climate simulations. By using approximations to gain execution speed, these codes sacrifice accuracy compared to more accurate, but also much slower, alternatives. International efforts to evaluate the approximate schemes have taken place in the past, but they have suffered from the drawback that the accurate standards were not validated themselves for performance. The manuscript summarizes the main results of the first phase of an effort called "Continual Intercomparison of Radiation Codes" (CIRC) where the cases chosen to evaluate the approximate models are based on observations and where we have ensured that the accurate models perform well when compared to solar and thermal radiation measurements. The effort is endorsed by international organizations such as the GEWEX Radiation Panel and the International Radiation Commission and has a dedicated website (i.e., http://circ.gsfc.nasa.gov) where interested scientists can freely download data and obtain more information about the effort's modus operandi and objectives. In a paper published in the March 2010 issue of the Bulletin of the American Meteorological Society only a brief overview of CIRC was provided with some sample results. In this paper the analysis of submissions of 11 solar and 13 thermal infrared codes relative to accurate reference calculations obtained by so-called "line-by-line" radiation codes is much more detailed. We demonstrate that, while performance of the approximate codes continues to improve, significant issues still remain to be addressed for satisfactory performance within GCMs. We hope that by identifying and quantifying shortcomings, the paper will help establish performance standards to objectively assess radiation code quality, and will guide the development of future phases of CIRC

[1] This paper reports on the canopy layer urban heat island (UHI) and human comfort in a range of small to large cities and villages in the Netherlands. To date, this subject has not been substantially studied in the Netherlands, since it has a relatively mild oceanic (Cfb) climate and impact was assumed to be minor. To fill this knowledge gap, this paper reports on observations of a selected network of reliable hobby meteorologists, including several in The Hague and Rotterdam. A number of alternative measures were also used to quantify UHI, i.e., the generalized extreme value distribution and return periods of UHI and adverse human comfort; its uncertainties were estimated by the statistical method of bootstrapping. It appeared essential to distinguish observations made at roof level from those made within the urban canyon, since the latter related more closely to exposure at pedestrian level and to urban canyon properties in their close neighborhood. The results show that most Dutch cities experience a substantial UHI, i.e., a mean daily maximum UHI of 2.3 K and a 95 percentile of 5.3 K, and that all cities experience a shadow effect in the morning when cities remain cooler than the rural surroundings. Also, an evident relation between the median of the daily maximum UHI and its 95 percentile was discovered. Furthermore, the 95 percentile of the UHI appears well correlated with population density. In addition, we find a significant decrease of UHI and the percentage of surface area covered by green vegetation, but the relation with open water remains unclear.

[1] The radiation budget in the Earth-atmosphere system is what drives Earth's climate, and thus measurements of this balance are needed to improve our knowledge of Earth's climate and climate change. In the present paper we focus on the analysis of the surface shortwave radiation budget (SSRB), which is the amount of energy in the solar region of the electromagnetic spectrum (0.2–4.0 μm) absorbed at the surface. The SSRB has to be modeled from the surface to the top of the atmosphere, jointly with information about the state of the atmosphere and the surface. These data come from satellites orbiting the Earth and are often missing or disturbed. Its interest is not only at global scales; rather, regional, high-spatial-resolution description is also of interest as an indicator of changes and because of its relationship to aridification from well-developed vegetation. The goal of this paper is to estimate and predict the spatiotemporal evolution of SSRB data at a regional scale in eastern Spain. Two different spatiotemporal models with covariates are considered: one is based on modeling the spatiotemporal semivariogram and the other uses the Kalman filter technique for spatiotemporal prediction. We present comparisons between these two models with respect to the simpler, purely spatial model. The results show that there is not a great benefit to use the more complicated models, although there is a marginal improvement with complexity.

Vb cyclones typically emerge in the Western Mediterranean and propagate to the Northeast into Central Europe. This paper explores the temporal characteristics of Vb cyclone occurrence based on cyclone tracks identified at the atmospheric levels of Z700 and sea level pressure, using JRA‐55 reanalysis data for the period 1959–2015. The risk of Vb occurrence was significantly high in the 1960s and has remained at a lower level since then. Vb cyclones do not occur fully randomly according to a Poisson point process. Eleven well‐separated and distinct clusters as well as 11 hiatus periods are identified, with average occurrence rates of 21.5 and 5.2 yrea−1, respectively. During the event of Vb, the large‐scale atmospheric circulation is changed into a state favoring the development of successive Vb cyclones. Clustering is very prominent in the case of Genoan Vb cyclones in summer as well as those Vb cyclones developing over the Iberian Peninsula or the North African Coast in winter. Superposition of the polar and the subtropical jet stream over the Western Mediterranean is identified as a main feature at the onset of Vb cyclones. Vb cyclone occurrence appears to be synchronized with the Northern Atlantic Oscillation (NAO; at Z500) and Arctic Oscillation (AO; at Z1000). Clusters have occurred when both NAO and AO were negative. This relation applies to Western Mediterranean cyclones not following a Vb track as well, however to a much weaker extent. In contrast, Vb cyclone frequency was particularly low from 1988 to 1997 during a sustained positive phase of both NAO and AO., Key Points In the past 50 years, the frequency of Vb cyclones was high in the 1960s and lower since thenVb cyclones were strongly synchronized with NAO and AO; high frequency clusters occurred when NAO and AO were negativeThe coupling of the polar and the subtropical jet stream over the Western Mediterranean is a driving mechanism of the onset of Vb cyclones

To alleviate water shortage in northern China, the middle route of the South to North Water Diversion Project (MRP) was constructed by the Chinese government. A dramatic reduction in the annual streamflow into Danjiangkou Reservoir (ASDR), the water source of MRP, during 1990 has raised some concerns on the MRP's operation. This paper employed an advanced segmented regression model with more recent data to have a clear picture and understand the changing pattern of the ASDR. Our study firstly revealed a zigzag changing pattern (decreasing-increasing-decreasing-increasing) of ASDR during 1960-2013, which was supported by statistical criteria compared with a monotonic or single abrupt change. Particularly, the significantly decreasing trend from 1990s was reversed after 2000, and such change may relieve the concern about the water availability in the future. Sensitivity analysis showed that changes in streamflow were largely influenced by the combined effects of precipitation (P) and potential evapotranspiration (ET0), and were more sensitive to P than ET0. As ET0 is estimated from other primary variables, further analysis was conducted to understand the sensitivities of ET0 to its primary driving variables (wind speed, actual vapor pressure, temperature and sunshine duration), and indicated that ET0 is mostly sensitive to actual vapor pressure during 1960-2013. The findings will assist the MRP's operation and management. Moreover, the results in this study also indicates that an adaptive water diversion plan, rather than the current plan with a constant annual amount of diversion water, might be a better option in the MRP's operation.

The recent International Panel on Climate Change report predicts the highly urbanized Northeastern U.S. to be at high risk to heat waves. Since urban residents and infrastructure are known to be highly vulnerable to extreme heat, the goal of this paper is to understand the interaction between the synoptic-scale heat wave and the city-scale urban heat island (UHI) effects. The study also qualitatively analyzes the primary factors that contribute to UHIs by comparing their intensities in different cities with distinct geo-physical characteristics. Our results, generated by using the Weather Research and Forecasting model augmented with advanced urban surface parameterizations, confirm that the amplitude of UHI is related to the physical size of the city. However, the results suggest that cities of comparabale sizes might interact differently with heat waves: in New York City; Washington, DC; and Baltimore (but not in Philadelphia) the regular UHI was amplified more strongly during heat waves compared to smaller cities. The results also establish that the pattern of UHI in different cities, its variability, and its interaction with heat waves are inherently linked to dynamic factors.

Biomass burning has a significant and adverse impact on air quality, climate change, and various ecosystems. The Hazard Mapping System (HMS) detects fires using data from multiple satellite sensors in order to maximize its fire detection rate. However, to date, the detection rate of the HMS fire product for small fires has not been well studied, especially using ground-based fire records. This paper utilizes the 2011 fire information compiled from ground observations and burn authorizations in Georgia to assess the comprehensiveness of the HMS active fire product. The results show that detection rates of the hybrid HMS increase substantially by integrating multiple satellite instruments. The detection rate increases dramatically from 3% to 80% with an increase in fire size from less than 0.02 km2 to larger than 2 km2, resulting in detection of approximately 12% of all recorded fires which represent approximately 57% of the total area burned. The spatial pattern of detection rates reveals that grid cells with high detection rates are generally located in areas where large fires occur frequently. The seasonal analysis shows that overall detection rates in winter and spring (12% and 13%, respectively) are higher than those in summer and fall (3% and 6%, respectively), mainly because of higher percentages of large fires (>0.19 km2) that occurred in winter and spring. The land cover analysis shows that detection rates are 2–7 percentage points higher in land cover types that are prone to large fires such as forestland and shrub land.

The U.S. National Oceanic and Atmospheric Administration (NOAA) has operated a program to continuously monitor atmospheric CO2 at Cape Matatula, American Samoa, since January 1976. This paper describes the basic operational program and reports the data through 1987. Data sets are derived from hourly means which have been selected to represent baseline conditions. All hourly mean CO2 values (with flags indicating data selection status) are archived at NOAA's Geophysical Monitoring for Climatic Change (GMCC) laboratory in Boulder, Colorado; at the Carbon Dioxide Information and Analysis Center in Oak Ridge, Tennessee; and in the microfiche version of this paper. The record from the in situ analyzer is compared with that of flask samples obtained in various ways. The overall 12-year record shows an average increase of 1.44 parts per million by volume per year. This increase amounts to 61% of the carbon dioxide emitted into the atmosphere by fossil fuel combustion during this period. The record is also characterized by the lack of a prominent seasonal cycle. The CO2 concentrations for the first half of each year are always more variable than the second 6 months, when the wind flow is dominated by strong southeasterly trades. The interannual variability of the CO2 growth rate correlates very well with that observed at other GMCC sites.

To provide input data for the potential future climate change impact assessment at regional levels, dynamic downscaling of global climate models (GCMs) climate using regional climate models (RCMs) is frequently utilized. It is important to understand how the climate change signal is modified in the RCM driven by GCM so that the simulated results can be accurately interpreted. This study compares the performance of a RCM and four driving GCMs in simulating precipitation over Africa and investigate how dynamically downscaled present and future climate from the regional model RegCM4.1 driven with four GCMs differ from those of the GCMs. In general, RegCM4.1 has a lower summer (June–August) precipitation bias than the driving GCMs, increasing our confidence in their future projections. Despite uncertainty in future projections, RegCM4.1 shows decreased precipitation over Sahel regardless of which GCM is used to drive the RCM. RegCM4.1 shows an increased interannual variability of summer precipitation over Gulf of Guinea and Central Africa as compared to GCMs in both present-day climate and projected future climate scenarios. The mean annual cycle of precipitation differs between RCM and the driving GCM, in particular during the West African Monsoon (WAM). The WAM onset, peak, and retreat phases are more clearly defined in the RegCM4.1 than the four driving GCMs, where the WAM rainband shifts more southward. This paper highlights the uncertainties in dynamical downscaling for climate prediction, which are due to the inconsistencies in the physical packages between RCM and GCMs.

Precipitation characteristics, such as intensity, frequency, duration, and event pattern, are changing due to the increases in greenhouse gases, transition of ocean oscillation phases, etc. In this paper, we evaluate the ability of 11 realizations from multiple regional climate model (RCM)/global climate model pairs in the North American Regional Climate Change Assessment Program (NARCCAP) to simulate the seasonal variability and magnitude of storm properties, including storm duration, interstorm period, and storm intensity. The results indicate that NARCCAP RCMs simulate the seasonal variability better in the Greater Sacramento and San Joaquin than in Las Vegas, which may be due to the RCMs' inability to simulate local convective precipitation associated with the North American Monsoon. We also investigate the impacts of climate change on these storm characteristics by comparing the percentage change and absolute change of storm properties determined from NARCCAP historical runs and future runs. We find that individual RCMs exhibit great uncertainty in the percentage changes in storm duration, interstorm period, and average storm intensity. The ensemble means of storm properties across 11 future NARCCAP RCM projections show different responses to climate change in different locations. Our analyses provide guidelines for selecting the appropriate RCMs for hydrologic studies related to storm properties and provide forecasters and water managers with detailed information of future changes in storm properties so that they can sustainably manage water resources. Our results may also contribute to the nonstationary precipitation scenario development by incorporating the percentage changes of storm properties caused by human-induced warming into the stochastic precipitation model.

Located on the Tibetan Plateau, the source region of Yellow River has experienced remarkable climate change over past a few decades, which affects the regional ecosystem, agricultural development, and water availability. In this paper, high-resolution RegCM3 driven by ECHAM5 is applied to generate both control climate for 1980-2000 and regional climate projections for the 21st century (2010-2098) under the Intergovernmental Panel on Climate Change (IPCC) A1B emission scenario. For control climate, RegCM3 can well reproduce the spatial patterns of precipitation and surface air temperature with more detailed representation of fine scale topography. Wet and cold biases are produced in the simulation, but overall improvement by RegCM3 is evident compared to the driving global climate model (GCM) of ECHAM5. In the future projection, the model demonstrates significant warming over the whole analysis domain. Precipitation on the other hand shows mixed signals of reduction and increase over simulation domain, while the areas of precipitation reduction extend with increasing integration time and finally covermost parts of the domain at the end of 21st century. As projection time increases, high altitude region will experience more precipitation reduction in summer and less reduction or even increase in winter. The winter warming at the high elevation area gets more evident than that at the low elevation area, which may be due to the snow feedback. Analyzing the change of probability distributions of surface climate, it can be concluded that the frequency of heavy precipitation in winter tends to increase with time indicating more extreme precipitation events in the future. The spectrum of temperature probability density functions (PDFs) moves toward higher end.

Extreme precipitation events over India have resulted in loss of human lives and damaged infrastructures, food crops, and lifelines. The inability of climate models to credibly project precipitation extremes in India has not been helpful to longer-term hazards resilience policy. However, there have been claims that finer-resolution and regional climate models may improve projections. The claims are examined as hypotheses by comparing models with observations from 1951-2005. This paper evaluates the reliability of the latest generation of general circulation models (GCMs), Coupled Model Intercomparison Project Phase 5 (CMIP5), specifically a subset of the better performing CMIP5 models (called "BEST-GCM"). The relative value of finer-resolution regional climate models (RCMs) is examined by comparing Coordinated Regional Climate Downscaling Experiment (CORDEX) South Asia RCMs ("CORDEX- RCMs") versus the GCMs used by those RCMs to provide boundary conditions, or the host GCMs ("HOST-GCMs"). Ensemble mean of BEST-GCMs performed better for most of the extreme precipitation indices than the CORDEX-RCMs or their HOST-GCMs. Weaker performance shown by ensemble mean of CORDEX-RCMs is largely associated with their high intermodel variation. The CORDEX-RCMs occasionally exhibited slightly superior skills compared to BEST-GCMs; on the whole RCMs failed to significantly outperform GCMs. Observed trends in the extremes were not adequately captured by any of the model ensembles, while neither the GCMs nor the RCMs were determined to be adequate to inform hydrologic design.

This paper presents the retrieval algorithm of nitrous oxide (N2O) using the Atmospheric Infrared Sounder (AIRS) on EOS/Aqua, its validation using aircraft measurements, and one possible application for monitoring the global N2O annual trend from 2003 to 2013. The results demonstrate that AIRS is sensitive to N2O in the middle to upper troposphere, with the peak vertical sensitivity between 200 and 750 hPa and the sensitivity in the tropics larger than in the high-latitude regions. The degrees of freedom of the N2O retrieval are mostly between 1.0 and 1.5. Validation using the aircraft measurement profiles by the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations program over the Pacific Ocean indicated that the retrieval RMS error is mostly less than 8 ppb (or ~2.1%). One important feature is that the variability of N2O from AIRS is more than 2 times than that of the aircraft measurements in the lower troposphere. In agreement with surface measurements, a nearly linear trend of N2O can be obtained based on limited AIRS data of 1 day in 15 May in each year from 2003 to 2013, and the increase rate of N2O is about 0.72 ppb yr−1. This algorithm will be implemented in AIRS operational retrieval system, enabling the derivation of the N2O for over 20 years using the AIRS, the Infrared Atmospheric Sounding Interferometer, and the Cross-track Infrared Sounder. Such a unique product will be complementary to currently sparse ground-based observations for monitoring the N2O trend associated with climate change.

Aerosols play a significant yet complex and central role in the Earth's radiation budget, and knowledge of long-term changes in the atmospheric turbidity induced by aerosols is therefore fundamental for a better understanding of climate change. However, there is little available information on changes in aerosol concentration in the atmosphere, especially prior to the 1980s. The present paper reviews publications reporting the suitability of sunshine duration records with regard to detecting changes in atmospheric aerosols. Some of the studies reviewed propose methods for estimating aerosol-related magnitudes, such as turbidity, from sunshine deficit at approximately sunrise and sunset, when the impact of aerosols on the solar beam is more easily observed. In addition, there is abundant evidence that one cause of the decadal changes observed in sunshine duration records involves variations in atmospheric aerosol loading. Possible directions for future research are also suggested: in particular, detailed studies of the burn (not only its length but also its width) registered by means of Campbell-Stokes sunshine recorders may provide a way of creating time series of atmospheric aerosol loading metrics dating back to over 120 years from the present.

Knowledge about long-term changes in climate extremes is vital to better understand multidecadal climate variability and long-term changes and to place today’s extreme events in a historical context. While global changes in temperature and precipitation extremes since the midtwentieth century are well studied, knowledge about century-scale changes is limited. This paper analyses a range of largely independent observations-based data sets covering 1901–2010 for long-term changes and interannual variability in daily scale temperature and precipitation extremes. We compare across data sets for consistency to ascertain our confidence in century-scale changes in extremes. We find consistent warming trends in temperature extremes globally and in most land areas over the past century. For precipitation extremes we find global tendencies toward more intense rainfall throughout much of the twentieth century; however, local changes are spatially more variable. While global time series of the different data sets agree well after about 1950, they often show different changes during the first half of the twentieth century. In regions with good observational coverage, gridded observations and reanalyses agree well throughout the entire past century. Simulations with an atmospheric model suggest that ocean temperatures and sea ice may explain up to about 50% of interannual variability in the global average of temperature extremes, and about 15% in the global average of moderate precipitation extremes, but local correlations are mostly significant only in low latitudes.

[1] Globally, land use/cover (LU) changes have occurred concurrently with climate change. Several studies have shown significant climate impacts of LU change through sensitivity experiments, which require idealized climate simulations with and without LU change forcings. This paper proposes a new methodology to assess LU change impacts that can be applied to common “all-forcings” experiments and does not require single-forcing LU change experiment. The proposed methodology is based on comparing climate change impacts between two neighboring regions in which one region has experienced LU change and the other has not. The methodology is applied to climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the 20th century climate simulations, all 15 climate models examined show a net increase in summer surface albedo, 11 out of 15 models show a net decrease in summer evapotranspiration, and 8 out of 15 models show a net increase in summer temperature in North America and Eurasia LU change regions. In the 8.5 Wm−2 representative concentration pathways (RCP8.5) 21st century climate simulations, all 14 CMIP5 climate models show more summer warming in North America and Eurasia historical LU change regions than in the surrounding regions. We found substantial uncertainties in Africa and South America future LU change regions, where most climate models show a net decrease in summer temperature due to LU change. These uncertainties could be due to the differences in regional climate characteristics, changes in land-atmosphere interaction in the 21st century, and/or methodological biases.

[1] The ratio of record highs to record lows and their rates of decay with time has emerged as a new metric for climatic warming and extremes complementing with the well-documented daily temperature range. This paper evaluates both the observed and model-projected high/low record behaviors in China, with a particular focus on an anomalous cooling region sometimes termed “warming hole.” We found that the observed frequency decay over time of high (low) record temperatures occurred much slower (faster) than the theoretically expected rate (1/n) of the independently and identically distributed (i.i.d.) time series. The high to low record (H/L) ratio is 3.0 in the recent years, about 50% larger than those in the U.S. and Europe. A drifting-mean i.i.d. model mimicking warming climate represents the observed record behavior much better than the i.i.d. sequence, but it still cannot explain the full record variability, implying that either the past mean warming rate has been nonlinear (accelerating) or temperature variance has become more extreme. The H/L record ratio by the mid-2040s with respect to 2006 could reach 6.8 under the high emission RCP8.5 scenario, whereas the increase of the record magnitude in the representative concentration path scenario remains largely similar to the current climate. Record frequency is highly correlated not only to Pacific Warm Pool temperature but also to Atlantic Multidecadal Oscillation index, with significant positive correlation in northern and southeastern China.

[1] Changes in river discharge and river water quality, due to climate change and other drivers such as land cover change, pose both societal and ecosystem threats. Analyses of measured terrestrial river fluxes are key for identifying the drivers and quantifying the magnitudes of such riverine changes. In this paper, we develop and apply a data-driven regionalization approach using the dense network of discharge measurements in Sweden. The developed regionalization approach facilitates detailed mapping of discharges (Q) and change trends in Q across Sweden. Combining these with estimates of precipitation (P) and change trends in P, we estimated actual evapotranspiration (AET) and change trends in AET via catchment-scale water balance constraints. We identified characteristic land cover–evapotranspiration relationships by plotting water use efficiency (AET/P) against energy use efficiency (AET/potential ET) for areas with unique land cover across Sweden. Our results show that wetlands have clearly lower water and energy use efficiencies compared to open waters, forests, and agriculture, and that agriculture has water and energy use efficiencies closest to those of open waters. We further compared the data-driven regionalization estimates of different water balance components with estimates of regional climate models (RCMs). The RCMs do not describe well the observed change trends in Sweden. In particular, for evapotranspiration, the observed change trends are not reproduced by any of the investigated 24 RCMs.

Many observational studies have demonstrated that the amplitude of the seasonal carbon cycle in high latitudes is increasing and advancing in phase. Several hypotheses related to natural and anthropogenic landscape disturbance and climate change have been proposed to explain these observations. This study employs a regional atmosphere-biosphere model (the Arctic Regional Climate System Model (ARCSyM)) to investigate the impacts of the climatic effects of elevated carbon dioxide on terrestrial carbon exchange over Alaska. A perturbation experiment, driven by a “transient” global climate scenario at the point at which atmospheric carbon dioxide has doubled, is compared to a validated present-day simulation. The focus of this paper is on the role of physical climate in seasonal carbon exchange between the atmosphere and the terrestrial biosphere at regional scales. In the present-day simulation the ARCSyM reproduces most observed features of climate and the surface energy budget and simulates a clearly defined growing season in the seasonal cycle of carbon dioxide exchange. In the perturbation experiment, the model atmosphere shows a generally warmer and wetter climate, with increased permafrost thaw. Two dominant biomes, tundra and boreal forest, are analyzed in detail for their responses to the perturbed climate. The amplitudes of the seasonal carbon dioxide cycle for Alaskan boreal forest and tundra increase 12 and 8%, respectively, with phase advances of ∼7 and 2 days, respectively. The forcing of photosynthetic productivity by soil moisture changes is a dominant contributor to the simulated changes in net ecosystem exchange. Boreal forest ecosystem is more sensitive to moisture anomalies due to a drier soil, whereas in the tundra regions, which are often close to saturation, changes are forced primarily by temperature variations. Thus in the event of a northward migration of tree line, sensitivity of regional carbon exchange to climate perturbations could increase.

Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical downscaling, the latter being especially attractive in ensemble predictions due to its computational efficiency. Most existing precipitation downscaling schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the downscaled values were to be used in a coupled atmospheric-hydrologic model. In this paper we propose a rainfall downscaling model which utilizes the presence of dynamic scaling in rainfall [Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.

The calculation of global land surface air temperature trends using the instrumental record has been based primarily upon two methods of maximizing the availability of station records. Hansen and Lebedeff[1987] developed a technique that is still used today, known as the reference station method; Jones et al. [1986a] popularized the climate anomaly method in their calculations of global temperature trends. In this paper we introduce yet another approach designed to maximize station records, referred to as the first difference method. To test the sensitivity of global temperature trend analysis to the method used, we calculate worldwide-averaged land surface mean temperature using each of these methods with an identical data base, the Global Historical Climatology Network. For further comparisons, a global climate model (GCM) transient model simulation is interpolated to the Global Historical Climatology Network station locations and the three techniques are then applied to data interpolated to the station locations from the model. The Intergovernmental Panel on Climate Change (IPCC); [Nicholls et al., 1996] estimated a global land and ocean temperature change of 0.45°C ± 0.15°C since the 19th century. Their assessment of the uncertainty associated with this temperature trend did not specifically address the differences that the method of calculating a global temperature time series might produce. Our results indicate that the differences in 1880-1990 trends produced by these three different methods are only a few hundredths of a degree centigrade per 100 years on trends of approximately 0.5°C/100 years. This is quite small compared to the 0.15°C/100 years uncertainty associated with the IPCC global land and ocean assessment which included factors such as data homogeneity which are not addressed here. Indeed, our results indicate that the source of differences in trends is more likely to be the method used to calculate a linear trend from a global temperature time series than the method used to create the global temperature time series. The modeled results confirm this finding but highlight other important characteristics: the reference station method has uncharacteristically low interannual variance, more similar to time series from the entire globe (land and ocean) than the global land area from which the data were observed. This lower variance can impact the statistical significance associated with linear trends.

The Milankovitch insolation equation as an astronomical forcing function for climate change is modified to include the frequency variation effect of the Earth's obliquity on insolation. To establish the criterion that frequency variation of the obliquity is responsible for major paleoclimatic changes, the cooperative phenomena between the obliquity frequency and the Milankovitch insolation are defined as frequency-insolation resonances. Coupling of the orbital frequency and insolation variations has established an especially new and interesting series of these resonances, which may induce pulsation of insolation. In this paper we show that insolation pulses would modulate the Milankovitch insolation in such a way that the mode (phase or period) of the insolation variations could be locked into a 100-kyr cycle. The pulse-modulated Milankovitch insolation forcing is tested by energy balance climate models. The physical mechanism behind this forcing for glacial-interglacial changes is explained and clarified in terms of pulse duration in the incoming solar radiation. It is concluded that the frequency-insolation resonances may be the key to the puzzle of the 100-kyr paleoclimate cycle.

Tropospheric O 3 is an important greenhouse gas. Lightning is a major source of NO x , and thus of tropospheric O 3 It has recently been suggested that due to an apparent strong correlation between lightning strike rates and surface temperatures, tropospheric O 3 may significantly increase if the climate warms, resulting in a substantial positive climate forcing. This paper attempts to quantify the extent of this forcing and the associated positive climate feedback. Simulations incorporating a tropospheric 03-surface temperature parameterization are performed of the last glacial maximum and of a climate in which greenhouse gas concentrations have been doubled. The O 3 parameterization is based on results from a two-dimensional chemical model. The simulations are obtained using a one-dimensional radiative-convective model, in which CO 2 , CH 4 , tropospheric O 3 , and in the case of the paleoclimate simulation the surface albedo, are varied both independently and in combination. In the paleoclimate case, the tropospheric O 3 feedback has roughly two thirds of the effect on surface temperatures as the change in tropospheric O 3 due to industrialization alone. During climate warming, the effect on surface temperatures is about 60% of that due to a doubling of CH 4 . The results indicate that a surface temperature-lightning-O 3 feedback, currently absent in general circulation models, could significantly affect anthropogenic climate change. Improved modeling and observations are required to confirm this.

A mass balance program was initiated in the Vestfjella-Heimefrontfjella area of western Dronning Maud Land during the austral summer of 1988–1989. As a part of this program, spatial and temporal variations in snow accumulation and temperature/stable isotopes are measured using shallow firn cores. In this paper we present surface accumulation data and discuss the climatic implications of the stable isotope records from two shallow firn cores. One 30-m-deep core, obtained about 200 km from the coast at 700 m asl (73°36′S, 12°26′W), covers the period 1932–1991. The other core was drilled at about 500 km from the coast at 2900 m asl (75°00′S, 2°00′E) and covers the period 1865–1991. The recent accumulation increase that has been reported from several areas of the Antarctic continent is not present in either of these records. Instead, our coastal record suggests a significant decrease in accumulation, with the strongest trend from about 1975. There is a positive trend in the oxygen isotope signal in both cores, which in the coastal core corresponds to a temperature increase of about 1.8°C since the early 1930s, and in the high-altitude core to about 0.8°C since 1865. However, it is likely that part of this increase in δ18O is due to a change of moisture source.

[1] This paper investigates the long-term variability of specific weather types that are associated with damaging hailstorms in Germany for past (1971–2000) and future (2011–2050) time periods. Forty large-scale weather types are determined by the objective weather type classification scheme of German Weather Service. This scheme is applied to both reanalyses (ERA-40) and eight different regional climate model (RCM) simulations. It is shown that the RCMs are able to approximately reproduce the distribution of weather type occurrences obtained from the reference of ERA-40. Using additional insurance loss data, the weather types are further identified as hail-related or hail-unrelated. Hailstorms are neither captured comprehensively by existing observation systems nor can they be modeled reliably and the large-scale weather types are here considered as proxies for hail occurrence. Four weather types that are most likely associated with damaging hailstorms show a slight increase both during the past and future period according to the RCM simulations. A novel statistical model is developed for the probabilistic prediction of the fraction of hail damage days conditional on the weather types. The model is Bayesian and uses a Markov Chain Monte Carlo approach. For the ERA-40 reanalysis the model prediction agrees well with fraction of hail damage days observed in the insurance data. For most of the RCM projections, the statistical model predicts a slight increase in the number of hail days in the future (2031–2045), with relative changes between 7 and 15% compared to the period 1971–2000.

[1] It is now well understood that the Arctic is particularly sensitive to climate change. Arctic sea ice is already undergoing significant changes. Some of the recent decrease in sea ice extent is due to changes in the surface energy budget, including the effect of clouds. Accurate ground-based measurements of atmospheric and cloud properties are valuable for estimating components of the surface energy budget. In this paper, measurements made between 2006 and 2008 at the Canadian Network for the Detection of Arctic Change (CANDAC) site at Eureka, Nunavut, Canada (80°N, 86°W) and the Atmospheric Radiation Measurement (ARM) program site at Barrow, Alaska (71°N, 156°W) are used to examine differences in the atmospheres over the two sites, including the temperature, humidity, winds, and the downwelling longwave radiation flux. A method is developed to convert infrared radiances to downwelling longwave fluxes since broadband measurements of flux were not available at Eureka during the study period; the method is validated by comparing the fluxes at Barrow to independent measurements made by a pyrgeometer. Comparisons of the derived fluxes show significant differences between the two sites. Eureka is consistently colder and drier than Barrow, and the infrared effect of clouds on the surface energy budget is less. To examine the meteorological conditions that cause such differences, the ERA-Interim reanalysis model is used; it is chosen over other models because it provides the best reproduction of longwave radiation at the surface. We find that the location of Eureka predisposes it to cold and dry air masses from the central Arctic Ocean and the Greenland Ice Sheet. In contrast, the air masses at Barrow come from a variety of directions, some of which are relatively warm and moist.

This paper presents top-down constraints on the magnitude, spatial distribution, and seasonality of nitrous oxide (N{sub 2}O) emissions over the central United States. We analyze data from tall towers in 2004 and 2008 using a high resolution Lagrangian particle dispersion model paired with both geostatistical and Bayesian inversions. Our results indicate peak N{sub 2}O emissions in June with a strong seasonal cycle. The spatial distribution of sources closely mirrors data on fertilizer application with particularly large N{sub 2}O sources over the US Cornbelt. Existing inventories for N{sub 2}O predict emissions that differ substantially from the inverse model results in both seasonal cycle and magnitude. We estimate a total annual N{sub 2}O budget over the central US of 0.9-1.2 TgN/yr and an extrapolated budget for the entire US and Canada of 2.1-2.6 TgN/yr. By this estimate, the US and Canada account for 12-15% of the total global N{sub 2}O source or 32-39% of the global anthropogenic source as reported by the Intergovernmental Panel on Climate Change in 2007.

Rainfall is poorly modeled by general circulation models (GCMs) and requires appropriate downscaling for local-scale hydrological impact studies. Such downscaling methods should be robust and accurate (to handle, e.g., extreme events and uncertainties), but the noncontinuous and highly nonlinear nature of rainfall makes this task particularly challenging. This paper brings together and extends state-of-the-art methods into an integrated and robust probabilistic methodology to downscale local daily rainfall series from an ensemble of climate simulations. The downscaling is based on generalized linear models (GLMs) that relate monthly GCM-scale atmospheric variables to local-scale daily rainfall series. A cross-validation step ensures that the fitted models are correctly conditioned by the climate variables, and a statistical procedure is proposed to test whether the statistical relationships identified for the reference period also hold in a future perturbed climate (i.e., to test the stationarity assumption). Additionally, we propose a strategy to downweigh poorly performing GCM-GLM couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics including extremes and interannual variability. Furthermore, the projections are consistent with the simulations of physically based dynamical models. Using an original visualization method based on heat maps, we show that although the downscaling models were fitted at each of the 27 sites independently, their projections follow a spatially coherent pattern and that regions exhibiting different climate change impacts can be identified.

The anthropogenic increase in aerosol concentrations since preindustrial times and its net cooling effect on the atmosphere is thought to mask some of the greenhouse gas-induced warming. Although the overall effect of aerosols on solar radiation and clouds is most certainly negative, some individual forcing agents and feedbacks have positive forcing effects. Recent studies have tried to identify some of those positive forcing agents and their individual emission sectors, with the hope that mitigation policies could be developed to target those emitters. Understanding the net effect of multisource emitting sectors and the involved cloud feedbacks is very challenging, and this paper will clarify forcing and feedback effects by separating direct, indirect, semidirect and surface albedo effects due to aerosols. To this end, we apply the Goddard Institute for Space Studies climate model including detailed aerosol microphysics to examine aerosol impacts on climate by isolating single emission sector contributions as given by the Coupled Model Intercomparison Project Phase 5 (CMIP5) emission data sets developed for Intergovernmental Panel on Climate Change (IPCC) AR5. For the modeled past 150 years, using the climate model and emissions from preindustrial times to present-day, the total global annual mean aerosol radiative forcing is -0.6 W/m(exp 2), with the largest contribution from the direct effect (-0.5 W/m(exp 2)). Aerosol-induced changes on cloud cover often depends on cloud type and geographical region. The indirect (includes only the cloud albedo effect with -0.17 W/m(exp 2)) and semidirect effects (-0.10 W/m(exp 2)) can be isolated on a regional scale, and they often have opposing forcing effects, leading to overall small forcing effects on a global scale. Although the surface albedo effects from aerosols are small (0.016 W/m(exp 2)), triggered feedbacks on top of the atmosphere (TOA) radiative forcing can be 10 times larger. Our results point out that each emission sector has varying impacts by geographical region. For example, the single sector most responsible for a net positive radiative forcing is the transportation sector in the United States, agricultural burning and transportation in Europe, and the domestic emission sector in Asia. These sectors are attractive mitigation targets.

[1] Understanding changing trends and frequency of extreme rainfall and temperature events is extremely important for optimal planning in many sectors, including agriculture, water resource management, health, and even economics. For people living in the Jordan River region of the Middle East such changes can have immediate devastating impacts as water resources are already scarce and overexploited and summer temperatures in the desert regions can reach 45°C or higher. Understanding shifts in frequency and intensity of extreme events can provide crucial information for planning and adaptation. In this paper we present results from regional climate model simulations with RegCM3 and MM5 centered on the eastern Mediterranean region. Our analysis focuses on changes in extreme temperature and rainfall events. We show that maximum daily summer temperature is expected to increase by between 2.5°C and 3°C, with an increase in warm spell length. Precipitation extremes are expected to increase with longer dry spells, shorter wet spells, and increases in heavy rainfall. Model agreement for the control period 1961–1990 is higher in the southern region than in the north, perhaps because of the complex topography, suggesting that even small differences in spatial scale play an important role. In addition, we notice that the chosen global model plays an important role in determining future temperature trends, while the choice of regional climate model is critical for understanding how precipitation is expected to evolve.

[1] This paper discusses projections of the Indian summer monsoon (ISM) by the regional climate model COSMO-CLM, highlighting similarities to and differences from its driving model, the global atmosphere–ocean model ECHAM5/MPIOM. The ISM is quantified using the all-Indian monsoon rainfall (AIMR) index and two vertical wind shear indices. To investigate the impacts of greenhouse gas emissions on the ISM, four emission scenarios for the time period 1960–2100 (Special Report on Emissions Scenarios A2, A1B, B1, and commitment) are considered. The COSMO-CLM simulations show significantly weakening ISM trends in all indices for emission scenarios A2, A1B, and B1. Parts of northwestern India are projected to face a decrease in the monsoon rainfall amount of over 70% within this century. For the wind shear indices, the projected decreases are mainly due to changes in the upper troposphere winds. The weakening of the dynamics in the COSMO-CLM is in agreement with the weakening in the driving ECHAM5/MPIOM model. The two models further agree in significantly positive trends of atmospheric water vapor contents and rain day intensities. However, ECHAM5/MPIOM shows no decrease in AIMR. The different AIMR trends in the two models are found to be due to different changes in the residence time of water in the atmosphere: In the COSMO-CLM projections, the residence time is more prolonged than in ECHAM5/MPIOM. This again is the consequence of a decrease in the number of depressions moving toward the northwestern parts of India.

In order for clouds to be more accurately represented in global circulation models (GCM), there is need for improved understanding of the properties of ice such as the total water in ice clouds, called ice water content (IWC), ice particle sizes and their shapes. Improved representation of clouds in models will enable GCMs to better predict for example, how changes in emissions of pollutants affect cloud formation and evolution, upper tropospheric water vapor, and the radiative budget of the atmosphere that is crucial for climate change studies. An extensive cloud measurement campaign called CRYSTAL-FACE was conducted during Summer 2002 using instrumented aircraft and a variety of instruments to measure properties of ice clouds. This paper deals with the measurement of IWC using the Harvard water vapor and total water instruments on the NASA WB-57 high-altitude aircraft. The IWC is measured directly by these instruments at the altitude of the WB-57, and it is compared with remote measurements from the Goddard Cloud Radar System (CRS) on the NASA ER-2. CRS measures vertical profiles of radar reflectivity from which IWC can be estimated at the WB-57 altitude. The IWC measurements obtained from the Harvard instruments and CRS were found to be within 20-30% of each other. Part of this difference was attributed to errors associated with comparing two measurements that are not collocated in time an space since both aircraft were not in identical locations. This study provides some credibility to the Harvard and CRS-derived IWC measurements that are in general difficult to validate except through consistency checks using different measurement approaches.

Atmospheric CO2 measurements made with a nondispersive infrared analyzer during 1974–1985 at Mauna Loa Observatory, Hawaii, are described, with emphasis on the measurement methodology, calibrations, and data accuracy. Monthly mean CO2 data, representative of global background conditions, are presented for the period of record. The monthly means were derived from an all-data base of CO2 hourly averages archived at the National Oceanic and Atmospheric Administration (NOAA) Geophysical Monitoring for Climatic Change (GMCC) facility in Boulder, Colorado; at the Carbon Dioxide Information Analysis Center (CDIAC) in Oak Ridge, Tennessee; and in the microfiche version of this paper. Flags in the all-data base identify CO2 hourly averages that have been deemed unreliable because of sampling and analysis problems or that are unrepresentative of clean background air because of influences of the local environment, for example, CO2 uptake by nearby vegetation or contamination and pollution effects. The select NOAA GMCC monthly mean data are compared with similar data obtained independently at Mauna Loa Observatory by the Scripps Institution of Oceanography. The average difference of corresponding monthly mean CO2 values for the two data sets is 0.15±0.18 ppm, where the indicated variability is the standard deviation. Careful scrutiny of the NOAA GMCC measurement, calibration, and data processing procedures that might have caused the small bias in the data has revealed no unusual errors.