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Contributions to AGU's Capitol Cause Fund have been instrumental in efforts to inform members of the U.S. Congress and their staffs about a range of Earth and space science issues. Since its inception in 2004, these funds have provided support for AGU to conduct a number of educational briefings on Capitol Hill. These have included briefings about background levels of ozone in the atmosphere; the Intergovernmental Panel on Climate Change (IPCC) process; two AGU position statements: Human Impacts on Climate, and Renewing Investment in Ocean Research; and the AGU white paper Hurricanes and the Gulf Coast: Science and Sustainable Rebuilding. More than 3,000 AGU members contribute annually to the Capitol Cause fund. Their support helps sustain efforts to deliver sound science to Capitol Hill and to communicate directly with policy makers. Additional funds are needed to expand activities and member involvement.

The Dynamic Integrated Climate‐Economy (DICE) model is one of the most influential Integrated Assessment Models available. Its founder Professor William Nordhaus was recently awarded Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel due to his pioneering work on the economics of climate change. In a recent paper in American Economic Journal: Economic Policy, Nordhaus uses the model to conclude that a 2.5°C target is almost out of reach. In this paper, we update DICE 2016 R2 with state‐of‐the‐art models of the carbon cycle, heat uptake into the oceans, and the role of non‐CO2 forcers. We find that the allowable remaining carbon budget (over the period 2015–2100) to meet a 2.5°C target to be 2,360 GtCO2 whereas the estimate obtained using DICE 2016 R2 is about 460 GtCO2. Nordhaus's estimate of the remaining carbon budget for this target is hence five times lower than estimates made by our updated DICE. We also compare our results with estimates by the Intergovernmental Panel on Climate Change (IPCC), and find our results to be in line with the carbon budgets presented in IPCC SR 1.5. We explain the reasons behind the difference between our result and that of Nordhaus and propose that an updated climate module in DICE is warranted.

Geoengineering is the focus of a large debate over potential solutions to climate change. However, in the midst of geoengineering and other large‐scale proposals, such as reducing emissions at an industrial level, the role of individual actions to reduce emissions is often overlooked. Given the current and fast‐paced changes we have seen as emissions are reduced by COVID‐19 social distancing strategies, it is time to re‐examine the impact that individual actions can have. This paper considers how one individual action (reducing carbon dioxide emissions from gasoline‐fueled private vehicles), when adopted at a global scale, may have an effect that is comparable to the effects of geoengineering. This paper also argues that the role of geoengineering as a safeguard against climate change may be encouraging complacency and reducing motivation for individual action.

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models., Key Points The United Kingdom has developed its first community Earth system model and delivered a traceable hierarchy of models to CMIP6We applied a process‐based evaluation strategy in model development but chose not to use historic trends or measures of climate responseThe U.K. models exhibit higher climate sensitivity than seen in CMIP5 arising in part from more positive cloud feedbacks

This paper proposes a stochastic evaluation method for examining tree population states in a river cross section using an integrated model with Monte Carlo simulation. The integrated model consists of four processes as sub-models, i.e., tree population dynamics, flow discharge stochasticity, stream hydraulics, and channel geomorphology. A floodplain of the Kako River in Japan was examined as a test site, which is currently well vegetated and features many willows that have been growing in both individual size and overall population over the last several decades. The model was used to stochastically evaluate the effects of hydrologic and geomorphologic changes on tree population dynamics through the Monte Carlo simulation. The effects including the magnitude of flood impacts and the relative change in the floodplain level are examined using very simple scenarios for flow regulation, climate change, and channel form changes. The stochastic evaluation method revealed a tradeoff point in floodplain levels, at which the tendency of a fully vegetated state switches to that of a bare floodplain under small impacts of flood. It is concluded from these results that the states of tree population in a floodplain can be determined by the mutual interactions among flood impacts, seedling recruitment, tree growth, and channel geomorphology. These interactions make it difficult to obtain a basic understanding of tree population dynamics from a field study of a specific floodplain. The stochastic approach used in this paper could constitute an effective method for evaluating fundamental channel characteristics for a vegetated floodplain. This article is protected by copyright. All rights reserved.

PUBLICATIONS Reviews of Geophysics REVIEW ARTICLE 10.1002/2015RG000510 Key Points: • Negative effects of all global change factors were found for humid tropical forest biogeochemical processes • All global change factors except carbon dioxide fertilization are likely to promote warming and/or greenhouse gas emissions • Effects of drying and deforestation are relatively clear; effects of CO 2 fertilization and N deposition are less certain Supporting Information: • Supporting Information S1 • Table S1 Correspondence to: D. F. Cusack, dcusack@geog.ucla.edu Citation: Cusack, D. F., J. Karpman, D. Ashdown, Q. Cao, M. Ciochina, S. Halterman, S. Lydon, and A. Neupane (2016), Global change effects on humid tropical forests: Evidence for biogeochemical and biodiversity shifts at an ecosystem scale, Rev. Geophys., 54, doi:10.1002/ 2015RG000510. Received 31 OCT 2015 Accepted 5 JUL 2016 Accepted article online 12 JUL 2016 Global change effects on humid tropical forests: Evidence for biogeochemical and biodiversity shifts at an ecosystem scale Daniela F. Cusack 1 , Jason Karpman 2 , Daniel Ashdown 1 , Qian Cao 1 , Mark Ciochina 1 , Sarah Halterman 1 , Scott Lydon 1 , and Avishesh Neupane 1 Department of Geography, University of California, Los Angeles, California, USA, 2 Department of Urban Planning, University of California, Los Angeles, California, USA Abstract Government and international agencies have highlighted the need to focus global change research efforts on tropical ecosystems. However, no recent comprehensive review exists synthesizing humid tropical forest responses across global change factors, including warming, decreased precipitation, carbon dioxide fertilization, nitrogen deposition, and land use/land cover changes. This paper assesses research across spatial and temporal scales for the tropics, including modeling, field, and controlled laboratory studies. The review aims to (1) provide a broad understanding of how a suite of global change factors are altering humid tropical forest ecosystem properties and biogeochemical processes; (2) assess spatial variability in responses to global change factors among humid tropical regions; (3) synthesize results from across humid tropical regions to identify emergent trends in ecosystem responses; (4) identify research and management priorities for the humid tropics in the context of global change. Ecosystem responses covered here include plant growth, carbon storage, nutrient cycling, biodiversity, and disturbance regime shifts. The review demonstrates overall negative effects of global change on all ecosystem properties, with the greatest uncertainty and variability in nutrient cycling responses. Generally, all global change factors reviewed, except for carbon dioxide fertilization, demonstrate great potential to trigger positive feedbacks to global warming via greenhouse gas emissions and biogeophysical changes that cause regional warming. This assessment demonstrates that effects of decreased rainfall and deforestation on tropical forests are relatively well understood, whereas the potential effects of warming, carbon dioxide fertilization, nitrogen deposition, and plant species invasions require more cross-site, mechanistic research to predict tropical forest responses at regional and global scales. 1. Introduction Humid tropical forests are one of the planet’s greatest natural resources, serving as a terrestrial warehouse for organic carbon (C), protecting nearby communities from runoff and soil erosion, and providing habitat for a spectacular diversity of living organisms. The potential for anthropogenic climate change to disrupt ecosys- tem processes has long been recognized [Vitousek, 1994], but the effects of individual and interacting global change factors have not been comprehensively reviewed. The need for such a review is particularly urgent. Human populations and the extraction of resources continue to increase [Lutz et al., 2001; Watson et al., 2001], and a suite of global-scale consequences are rapidly unfolding on ecosystems across the globe. While climate change is broadly recognized as the most important global change that humans and natural ecosystems face, other factors are also likely to have important effects on all ecosystems, with unique and potentially more accelerated effects on tropical ecosystems. This paper is organized around different aspects of global change that are impacting humid tropical ecosystems. The global change factors are presented in two broad groups that each includes several specific aspects of change. Within (2.1) Changing Cycles we include (2.1.1) Climate Change, (2.1.2) CO 2 Fertilization, and (2.1.3) Nitrogen (N) Deposition. Within (2.2) Land Use/Land Cover Change we include (2.2.1) Deforestation and (2.2.2) Unmanaged Land Cover Change, which explore successional trajectories of lands abandoned postdeforestation. A brief introduction to each of these global change factors is provided at the beginning of each section. ©2016. American Geophysical Union. All Rights Reserved. CUSACK ET AL. Within each of these five sections, we synthesize how a particular global change factor is currently altering key ecosystem processes and properties. We focus on five ecosystem properties that are crucial for ecosystem function and which will determine whether tropical humid forests of the future will resemble those of the past TROPICAL FORESTS AND GLOBAL CHANGE

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.

Smoke from wildfires has adverse biological and social consequences, and various lines of evidence suggest that smoke from wildfires in the future may be more intense and widespread, demanding that methods be developed to address its effects on people, ecosystems, and the atmosphere. In this paper, we present the essential ingredients of a modeling system for projecting smoke consequences in a rapidly warming climate that is expected to change wildfire regimes significantly. We describe each component of the system, offer suggestions for the elements of a modeling agenda, and provide some general guidelines for making choices among potential components. We address a prospective audience of researchers whom we expect to be fluent already in building some or many of these components, so we neither prescribe nor advocate particular models or software. Instead, our intent is to highlight fruitful ways of thinking about the task as a whole and its components, while providing substantial, if not exhaustive, documentation from the primary literature as reference. This paper provides a guide to the complexities of smoke modeling under climate change, and a research agenda for developing a modeling system that is equal to the task while being feasible with current resources.

[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.

[1] Climate change is believed to pose potential risks to the stakeholders of the Great Lakes due to changes in lake levels. This paper presents a model of stakeholder-defined risk as a function of climate change. It describes the development of a statistical model that links water resources system performance and climate changes developed for the Great Lakes of North America. The function is used in a process that links bottom-up water system vulnerability assessment to top-down climate change information. Vulnerabilities are defined based on input from stakeholders and resource experts and are used to determine system performance thresholds. These thresholds are used to measure performance over a wide range of climate changes mined from a large (55,590 year) stochastic data set. The performance and climate conditions are used to create a climate response function, a statistical model to predict lake performance based on climate statistics. This function facilitates exploration and analysis of performance over a wide range of climate conditions. It can also be used to estimate risk associated with change in climate mean and variability resulting from climate change. Problematic changes in climate can be identified and the probability of those conditions estimated using climate projections or other sources of climate information. The function can also be used to evaluate the robustness of a regulation plan and to compare performance of alternate plans. This paper demonstrates the utility of the climate response function as applied within the context of the International Upper Great Lakes Study.

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.

[1] This paper analyses the potential conflict between resilience of the Earth system and global freshwater requirements for the dual task of carbon sequestration to reduce CO2in the atmosphere, and food production to feed humanity by 2050. It makes an attempt to assess the order of magnitude of the increased consumptive water use involved and analyses the implications as seen from two parallel perspectives; the global perspective of human development within a “safe operating space” with regard to the definition of the Planetary Boundary for freshwater; and the social-ecological implications at the regional river basin scale in terms of sharpening water shortages and threats to aquatic ecosystems. The paper shows that the consumptive water use involved in the dual task would both transgress the proposed planetary boundary range for global consumptive freshwater use and would further exacerbate already severe river depletion, causing societal problems related to water shortage and water allocation. Thus, strategies to rely on sequestration of CO2 as a mitigation strategy must recognize the high freshwater costs involved, implying that the key climate mitigation strategy must be to reduce emissions. The paper finally highlights the need to analyze both water and carbon tradeoffs from anticipated large scale biofuel production climate change mitigation strategy, to reveal gains and impact of this in contrast to carbon sequestration strategies.

[1] In the companion to the present paper, the one-dimensional k-e lake model SIMSTRAT is coupled to a single-column atmospheric model, nicknamed FIZC, and an application of the coupled model to the deep Lake Geneva, Switzerland, is described. In this paper, the response of Lake Geneva to global warming caused by an increase in atmospheric carbon dioxide concentration (i.e., 2 × CO2) is investigated. Coupling the models allowed for feedbacks between the lake surface and the atmosphere and produced changes in atmospheric moisture and cloud cover that further modified the downward radiation fluxes. The time evolution of atmospheric variables as well as those of the lake's thermal profile could be reproduced realistically by devising a set of adjustable parameters. In a “control” 1 × CO2 climate experiment, the coupled FIZC-SIMSTRAT model demonstrated genuine skills in reproducing epilimnetic and hypolimnetic temperatures, with annual mean errors and standard deviations of 0.25°C ± 0.25°C and 0.3°C ± 0.15°C, respectively. Doubling the CO2 concentration induced an atmospheric warming that impacted the lake's thermal structure, increasing the stability of the water column and extending the stratified period by 3 weeks. Epilimnetic temperatures were seen to increase by 2.6°C to 4.2°C, while hypolimnion temperatures increased by 2.2°C. Climate change modified components of the surface energy budget through changes mainly in air temperature, moisture, and cloud cover. During summer, reduced cloud cover resulted in an increase in the annual net solar radiation budget. A larger water vapor deficit at the air-water interface induced a cooling effect in the lake.

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] Recommendation ITU-R P.530-13 provides an internationally recognized prediction model for the fading due to wet snow on low-elevation, terrestrial microwave links. An important parameter in this model is the altitude difference between the link and the rain height. The top of rain events is usually assumed to be 360 m above the zero-degree isotherm (ZDI). Above this height, hydrometeors are ice with low specific attenuation. Below this level, melting ice particles produce a specific attenuation up to 4 times that of the associated rain rate. A previous paper identified increasing ZDI height trends across northern Europe, North America and central Asia with slopes up to 10 m/yr. This paper examines NOAA National Centers for Environmental Prediction–National Center for Atmospheric Research Reanalysis 1 data to identify global distributions of ZDI height around mean levels that increase linearly over time. The average annual distribution of ZDI heights relative to the annual mean are calculated for each NOAA Reanalysis grid square and skew normal distributions are fitted. These are compared to models in Recommendation ITU-R P.530-13 and Recommendation ITU-R 452-14. The effects of ZDI trends and the calculated skew normal distributions are illustrated using calculated trends in fading due to wet snow for two notional 38 GHz links in Edinburgh. A slow decrease in the incidence of fading due to wet snow is predicted over most of Europe. However, some links could experience increases where warming has increased the wetness of snow.

[1] Although information about climate change and its implications is becoming increasingly available to water utility managers, additional tools are needed to translate this information into secondary products useful for local assessments. The anticipated intensification of the hydrologic cycle makes quantifying changes to hydrologic extremes, as well as associated water quality effects, of particular concern. To this end, this paper focuses on using extreme value statistics to describe maximum monthly flow distributions at a given site, where the nonstationarity is derived from concurrent climate information. From these statistics, flow quantiles are reconstructed over the historic record and then projected to 2100. This paper extends this analysis to an associated source water quality impact, whereby the corresponding risk of exceeding a water quality threshold is examined. The approach is applied to a drinking water source in the Pacific Northwest United States that has experienced elevated turbidity values correlated with high streamflow. Results demonstrate that based on climate change information from the most recent Intergovernmental Panel on Climate Change assessment report, the variability and magnitude of extreme streamflows substantially increase over the 21st century. Consequently, the likelihood of a turbidity exceedance increases, as do the associated relative costs. The framework is general and could be applied to estimate extreme streamflow under climate change at other locations, with straightforward extensions to other water quality variables that depend on extreme hydroclimate.

[1] In our recent paper [Wang and Lee, 2008] (henceforth WL08), we perform an empirical orthogonal function (EOF) analysis on the global annual mean SST over the past 153 years (from 1854 to 2006). The first three EOF modes, which account for 28.3%, 15.3%, and 5.3% of the total variance in the SST data, may represent global warming, ENSO-like (including the Pacific decadal oscillation), and the Atlantic multidecadal oscillation (AMO), respectively. We then use the first EOF mode to study the relationship among global warming, vertical wind shear in the Atlantic hurricane main development region (MDR) and U.S. landfalling hurricanes. The second EOF mode (ENSO-like) and the third EOF mode (the AMO) are presented in another paper (see Figure 1 of Wang et al. [2008a] for these two modes). [2] In his comment, Barsugli [2009] (henceforth B09) points out that the SST spatial pattern of the first EOF mode is similar to that of an El Nino event and that its time series contains variations on interannual timescale. He thus concerns that the first EOF mode of global warming may include the interannual ENSO signal and therefore the observed relationship between global warming and U.S. landfalling hurricanes (also vertical wind shear in the hurricane MDR) of WL08 may be partly attributed to interannual ENSO’s effect. We appreciate and understand his concern since there is no ‘‘perfect’’ method for separating climate modes on various timescales from observational data such as global warming and ENSO modes. We agree that the first EOF mode contains some interannual variations possibly linked to ENSO and that it is difficult to distinguish anthropogenic climate change from natural lowfrequency variability in observational data. However, here we show that the conclusion of WL08 is still true even after removing interannual signals from the first EOF mode. We also present some additional evidences that support the conclusion of WL08. [3] B09 suggests that interannual variations are first removed by applying an 11-year running mean to the SST data prior to performing the EOF analysis. We agree that this approach may be a reasonable way to remove the interannual ENSO signal although it still keeps lowerfrequency variations of ENSO. We re-perform our EOF analysis following B09’s suggestion. The resulting spatial pattern and time series of the first EOF mode are shown in Figures 1a and 1b (Figure 1a is almost identical to Figure 1b of B09), which accounts for 57.4% of the total variance. In comparison with Figure 1 of WL08, the amplitude of SST in

[1] This paper documents various unresolved issues in using surface temperature trends as a metric for assessing global and regional climate change. A series of examples ranging from errors caused by temperature measurements at a monitoring station to the undocumented biases in the regionally and globally averaged time series are provided. The issues are poorly understood or documented and relate to micrometeorological impacts due to warm bias in nighttime minimum temperatures, poor siting of the instrumentation, effect of winds as well as surface atmospheric water vapor content on temperature trends, the quantification of uncertainties in the homogenization of surface temperature data, and the influence of land use/land cover (LULC) change on surface temperature trends. Because of the issues presented in this paper related to the analysis of multidecadal surface temperature we recommend that greater, more complete documentation and quantification of these issues be required for all observation stations that are intended to be used in such assessments. This is necessary for confidence in the actual observations of surface temperature variability and long-term trends.

[1] Debate persists as to the extent to which recent glacial recession observed in tropical highlands is driven primarily by changes in air temperature [e.g., Bradley et al., 2006; Thompson et al., 2006] and atmospheric humidity [e.g. Kaser et al., 2004; Molg and Hardy, 2004]. Uncertainty has also been expressed in the relationship between temperature trends at the surface and higher elevations in the tropical free troposphere [e.g., Christy et al., 2003; Christy and Norris, 2004; Douglass et al., 2004; Fu et al., 2004; Tett and Thorne, 2004] where alpine glaciers reside. We therefore welcome the constructive comments of Molg et al. [2006] regarding our original paper and appreciate the opportunity to clarify arguments made therein [Taylor et al., 2006]. We agree with Molg et al. that the surface energy balance and mass balance are best able to describe the relationship between climate parameters and glacier change [e.g., Wagnon et al., 1999; Molg and Hardy, 2004]. For the Rwenzori Mountains, measurements that would form the basis of a glacier mass balance model do not exist. This point was recognized explicitly in the original paper, ‘‘The absence of continuous and proximate meteorological observations in the Rwenzori Mountains prevents direct analysis of the climatic factors driving observed glacial recession.’’ Although a definitive, quantitative understanding of the climate variables responsible for glacier mass losses in the Rwenzori Mountains remains elusive, we dispute the assertion of Molg et al. that air temperature (Ta) is unlikely to be the main driver of observed glacial recession and argue that trends of increasing air temperature are better supported by currently available evidence than decreasing humidity posited by Molg et al. [2] The essential scientific criticism of our paper by Molg et al. [2006] is the validity of the assumption that Ta trends observed in gridded CRUTS 2.0 climate data sets [New et al., 2002] and at meteorological stations between 960 and 1869 meters above sea level (masl), reflect Ta trends in the middle troposphere (4800 to 5100 masl) where glaciers in the Rwenzori Mountains occur. Molg et al. suggest that we have disregarded evidence of inconsistencies between Ta trends at the surface and in the tropical troposphere, but the literature [Hense et al., 1988; Gaffen et al., 2000; Bradley et al., 2004] and evidence they cite is selective. Significant uncertainty persists in temperature data for the tropical troposphere whether these derive from satellite-borne Microwave Sounding Unit (MSU) observations or in situ measurements using radiosondes, particularly in data-poor regions like East Africa. Indeed, linear Ta trends in the tropical troposphere can vary significantly based simply upon choice of start and end date as is the case in the paper by Gaffen et al. [2000] using MSU data in which at 500 hPa a cooling trend is detected between 1979 and 1997 but an overall warming trend occurs between 1960 and 1997. Nevertheless, recent studies that employ diurnal corrections to MSU observations between 1979 and 2003 [Mears and Wentz, 2005] and homogenized radiosonde data sets (HadAT2) between 1958 and 2002 [Thorne et al., 2005], show that the middle troposphere warmed at a similar or slightly greater rate to the surface in the tropics [Fu and Johanson, 2005; Santer et al., 2005], consistent with the sign and (within error) magnitude of Ta trends (+0.13 C per decade) at the surface from climate model (HadCRU2v) predictions [Jones and Moberg, 2003]. [3] Molg et al. [2006] use NCEP reanalysis data [Kalnay et al., 1996] for the grid cell (30 E, 0 N) to support their claim that a discrepancy exists between Ta trends at the lower troposphere (850 hPa) and mid-troposphere (600 hPa) in the Rwenzori Mountains (their Figure 1). There is, however, widespread consensus within the climate community that reanalysis data are unsuitable for trend analysis in climate change studies as ‘‘. . .known discontinuities in reanalyzed data sets indicate that further research is required to reduce time-dependent errors to a level suitable for climate change studies’’ [Intergovernmental Panel on Climate Change, 2001, p. 120]. The existence of systematic, time-varying biases in reanalysis data is also highlighted by more recent studies [Bengtsson et al., 2004; Simmons et al., 2004; Sterl, 2004; Thorne et al., 2005]. Molg et al. consider biases in the NCEP data associated with the introduction of satellite observations in 1979 to reanalysis data sets (see caption in their Figure 1) but not other inconsistencies that arise from the wide range of data sources including modeled processes [Pepin and Seidel, 2005]. In contrast to inferences drawn by Molg et al. using NCEP data, upper air temperature records from gridded HadAT2 radiosonde data [Thorne et al., 2005] for the most proximate (and only) grid cell to the Rwenzori GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L20405, doi:10.1029/2006GL027606, 2006 Click Here for Full Article

[1] The increasing expression of human activity, climate variability, and climate change on humid, terrestrial hydrologic systems has made the integrated nature of large river basins more apparent. However, to date, there is no instrument platform sufficient to characterize river basins' hydrologic couplings and feedbacks, with many processes and impacts left almost entirely unobserved (e.g., snowmelt floods). Characterization at the river basin scale will require a more holistic vision and a far greater commitment from the environmental science community. It will require new designs and implementation of integrated instrumentation, a new generation of models, and a management framework that clearly addresses the human-climate-terrestrial interactions impacting our watersheds and river basins. Initially, we propose that existing “similarity classifications” (e.g., regional soil, geologic, ecologic, hydrographic digital products) can provide a starting point for organizing historical data and initiating a long-term adaptive, multiscale observing strategy. This vision paper outlines instrumentation platforms for point, plot, reach, and hillslope scales that could be located within the “characteristic” landscapes of river basins. The network of observing platforms then forms the basis of a “Hydro-Mesonet” that can potentially support multiscale, multiprocess scientific studies necessary to understand and improve forecasts of our water resources at the river basin scale. This paper concludes with a discussion of how a network of such sites can support research at the level of the individual researcher and scale to the level of community-wide initiatives.

[1] This article compares the major findings of eight synthesis papers resulting from a workshop to examine carbon cycle variability and climate change in the tropical and extratropical North Pacific Ocean. The workshop's intent was to encourage scientific exchange, synthesis activities, and the development of new ideas among scientists from different countries and backgrounds who do not regularly collaborate, but share a common interest in the Pacific. Six of the papers focus on air-sea CO2 flux variability on timescales from years to decades. While the average North Pacific surface waters show increases in CO2 that are comparable to the increase in atmospheric CO2 over time, considerable temporal and spatial variations are observed. A recurring theme of these studies is that throughout most of the Pacific, interannual to decadal variations in CO2 fluxes are primarily controlled by variations in physical forcing with changes in the biological cycle being of secondary importance. The dominant mode of variability is confirmed to be associated with the El Nino–Southern Oscillation phenomenon, particularly in the tropical Pacific. Extratropical air-sea CO2 flux variability is substantially smaller, primarily because dissolved inorganic carbon and temperature driven variations in the seawater pCO2 tend to compensate each other. Temporal variations in the wind fields are also important for assessing the variability in air-sea CO2 fluxes. The two papers addressing carbon and oxygen cycle changes within the water column also found that variability in physical forcing can explain the magnitude and patterns of observed variability in apparent oxygen utilization and other tracers.

[2] Polar amplification of CO2 forced warming is a common feature of climate change simulations. In transient simulations, southern hemisphere warming is retarded by the large heat uptake of the Southern Ocean, leaving the Arctic as the global location with the largest warming. This aspect of the global warming pattern has often been linked to surface albedo feedback (SAF) – the extra absorption of shortwave radiation as ice melts and the surface becomes less reflective. It is the goal of this paper to place the SAF in the context of other feedbacks and forcings that affect Arctic amplification. [3] Important work on this topic was done by Hall [2004] who showed, by disabling SAF in the GFDL climate model, that it accounts for part but not all of the polar amplification. Vavrus [2004] performed similar experiments with the GENESIS2 climate model to evaluate the role of cloud changes under doubled CO2. He found that the cloud fraction changes enhanced the warming at all latitudes but by a fractionally greater amount in the Arctic, therefore enhancing Arctic amplification. The high-latitude response to increased CO2 was found to be quite variable amongst the group of 15 CMIP climate models studied by Holland and Bitz [2003]. Using correlations, they identified a number of processes that contributed to the variation of Arctic amplification amongst the models. They found that models with larger increases in ocean heat transport, larger increases in cloud cover, and thinner control climate sea ice tended to have larger Arctic amplification. They proposed that thinner sea ice would lead to an increased ice-albedo feedback. However, Flato and CMIP Modelling Groups [2004] found that in the Southern Hemisphere, thinner ice was associated with reduced warming in the CMIP models. In the Southern Hemisphere, Flato and CMIP Modelling Groups [2004] found some tendency for models with more extensive ice to produce greater warming while in the Northern Hemisphere there was a tendency toward the opposite relationship. These studies emphasize the complexity of Arctic amplification and the multiplicity of processes that contribute to it. [4] In this paper the conventional energy balance method of global climate sensitivity analysis is applied to both global and Arctic regions. Two forcings and three feedbacks are diagnosed in each region. A comparison is then made of the impact of differences in the forcings and feedbacks between the two regions on the Arctic amplification. The simulations analyzed come from the archive of climate model results made for the IPCC fourth assessment report (AR4). The twelve AR4 models used here were chosen because they supplied the necessary data to calculate the SAF using a method developed by Winton [2005b]. Details on the twelve models and the SAF analysis method are given by Winton [2005a].

[1] Blooms of the coccolithophorid Emiliana huxleyi may be sensitive to climate change. A comparison of global Coastal Zone Color Scanner (CZCS, 1978–1986) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS, 1997–present) imagery appears to show them advancing into some sub-Arctic seas. To determine when coccolithophore blooms appeared in the Barents Sea this paper makes use of Advanced Very High Resolution Radiometer (AVHRR) visible channel data which bridges the gap between the CZCS and SeaWiFS missions (1981–present). Analysis of over 3700 AVHRR images has shown coccolithophore blooms to be unambiguously present between 1989–1992 but probably absent in other pre-SeaWiFS years. This paper shows a correlation between positive temperature - negative salinity anomalies in the Barents Sea and bloom occurrence. If global warming continues to trigger increased warmth and freshwater runoff in the region then there may be an increased frequency of coccolithophore blooms within the Barents Sea.

[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.

[1] Salt accumulation in soil is a global problem. This paper investigates salt-affected soils on a global scale through spatial modeling within a Geographical information system (GIS). As salt-affected soils arise due to a range of processes that can be very localized and complex in nature, three main indicators have been developed to provide insight into areas at risk of salinization at a global scale. At this scale, climate and topography are the main factors influencing solute accumulation. The paper derives salinity indicators from global data sets and compares them to independent maps of salt-affected areas. The primary indicators are (1) low relief, (2) high two-way annual moisture flux, and (3) local flow deficit in large catchments. The first two of these are related for conditions where there is potential for a large vertical flux of salts, and where groundwater levels may be high, providing conditions for salt accumulation locally or at the base of slope catenas. The third indicator is related to conditions where there is a risk of accumulation of salts in floodplains and adjacent irrigated areas. These salinity indicators are then estimated for future GCM scenarios of climate for 2079-2099. Comparison of the current and scenario indicator distributions shows that the areas at risk of salinization may increase globally, through both expansion of existing areas and the creation of new ones. Current distributions of risk, and to a greater extent future scenarios, are limited by the quality and resolution of available data, and have the potential for enhancement as these data improve.

Risk of climate‐related impacts results from the interaction of climate‐related hazards (including hazardous events and trends) with the vulnerability and exposure of human and natural systems. Despite the commitment of the Paris Agreement, the integrate research on climate change risk combining risk‐causing factors and risk‐bearing bodies, the regional differences in climate impacts are still missing. In this paper we provide a quantitative assessment of hazards and socioeconomic risks of extreme events, risks of risk‐bearing bodies in China under global warming of 1.5 and 2.0 °C based on future climate scenarios, and quantitative evaluation theory for climate change risk. For severe heat waves, hazards might significantly intensify. Affected population under 2.0 °C warming might increase by more than 60% compared to that of 1.5 °C. Hazards of severe droughts and floods might strengthen under Representative Concentration Pathway 8.5 scenario. Economic losses might double between warming levels of 1.5 and 2.0 °C, and the population affected by severe floods might continuously increase. Under the integrate effects of multiple disasters, the regions with high population and economic risks would be concentrated in eastern China. The scope would gradually expand to the west with socioeconomic development and intensification of extreme events. High ecological risks might be concentrated in the southern regions of the Yangtze River Basin, while the ecological risk in northern China would expand. High agriculture yield risks might be distributed mainly in south of the North China Plain, the Sichuan Basin, south of the Yangtze River, and west of Northwest China, and the risk levels might continuously increase.

Tropospheric aerosol radiative forcing has persisted for many years as one of the major causes of uncertainty in global climate model simulations. To sample the range of plausible aerosol and atmospheric states and perform robust statistical analyses of the radiative forcing, it is important to account for the combined effects of many sources of model uncertainty, which is rarely done due to the high computational cost. This paper describes the designs of two ensembles of the Met Office Hadley Centre Global Environment Model-U.K. Chemistry and Aerosol global climate model and provides the first analyses of the uncertainties in aerosol radiative forcing and their causes. The first ensemble was designed to comprehensively sample uncertainty in the aerosol state, while the other samples additional uncertainties in the physical model related to clouds, humidity, and radiation, thereby allowing an analysis of uncertainty in the aerosol effective radiative forcing. Each ensemble consists of around 200 simulations of the preindustrial and present-day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multimodel intercomparison projects. The mean aerosol effective radiative forcing is −1.45 W/m2 (credible interval of −2.07 to −0.81 W/m2), which encompasses but is more negative than the −1.17 W/m2 in the 2013 Atmospheric Chemistry and Climate Model Intercomparison Project and −0.90 W/m2 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The ensembles can be used to reduce aerosol radiative forcing uncertainty by challenging them with multiple measurements as well as to isolate potential causes of multimodel differences.

This is the first paper that analyzes data from atmosphere model intercomparison project‐type climate simulations using a cloud‐system‐resolving global nonhydrostatic model without cumulus parameterization focussing particulaly on the relationship between clouds and circulation, and their changes due to global warming. The decrease in fractional coverage of low clouds is key to evaluating cloud radiative effects, because changes in shortwave cloud radiative effects overwhelm those of longwave cloud radiative effects. Thus, improved evaluation of low clouds is important, even in high‐resolution climate simulations. An analysis of heat redistribution by explicitly computed clouds revealed that column‐integrated heating rate due to phase changes correlates highly with vertical velocity at the altitude corresponding to 500 hPa and is closely linked to column water vapor, similar to the present climate result. Using data from year 1 to year 5, the effective climate sensitivity was evaluated to be 3.6−3.7°C. Possible convective aggregation is also examined using an index of modified subsidence fraction and characteristic changes in the number of cold pools. Despite previous idealized‐planet simulations showing more aggregated tropical convection under warmer conditions, here we show a decrease in the subsidence fraction and an increase in the number of smaller cold pools, suggesting that it is possible to realize less convective organization with warming under real atmospheric conditions.

We face a new era in the assessment of multiple natural hazards whose statistics are becoming alarmingly non‐stationary due to ubiquitous long‐term changes in climate. One particular case is tsunami hazard affected by climate‐change‐driven sea level rise (SLR). A traditional tsunami hazard assessment approach where SLR is omitted or included as a constant sea‐level offset in a probabilistic calculation may misrepresent the impacts of climate‐change. In this paper, a general method called non‐stationary probabilistic tsunami hazard assessment (nPTHA), is developed to include the long‐term time‐varying changes in mean sea level. The nPTHA is based on a non‐stationary Poisson process model, which takes advantage of the independence of arrivals within non‐overlapping time‐intervals to specify a temporally varying hazard mean recurrence rate, affected by SLR. The nPTHA is applied to the South China Sea (SCS) for tsunamis generated by earthquakes in the Manila Subduction Zone. The method provides unique and comprehensive results for inundation hazard, combining tsunami and SLR at a specific location over a given exposure time. The results show that in the SCS, SLR has a significant impact when its amplitude is comparable to that of tsunamis with moderate probability of exceedance. The SLR and its associated uncertainty produce an impact on nPTHA results comparable to that caused by the uncertainty in the earthquake recurrence model. These findings are site‐specific and must be analyzed for different regions. The proposed methodology, however, is sufficiently general to include other non‐stationary phenomena and can be exploited for other hazards affected by SLR.

Californian hydroclimate is strongly seasonal and prone to severe water shortages. Recent changes in climate trends have induced shifts in seasonality, thus exacerbating droughts, wildfires, and adverse water shortage effects on the environment and economy. Previous studies have examined the timing of the seasonal cycle shifts mainly as temperature driven earlier onset of the spring season. In this paper, we address quantitative changes in the onset, amounts, and termination of the precipitation season over the past 6 decades, as well as the large-scale atmospheric circulation underpinning the seasonal cycle changes. We discover that the onset of the rainy season has been progressively delayed since the 1960s, and as a result the precipitation season has become shorter and sharper in California. The progressively later onset of the rainy season is shown to be related to the summer circulation pattern extending into autumn across the North Pacific, in particular, a delay in the strengthening of the Aleutian Low and later southward displacement of the North Pacific westerlies. Plain Language Summary The rainy season over California is projected to show a distinct sharpening of the mean seasonal cycle, with winter precipitation increasing, and both autumn and spring precipitation decreasing. Our analysis of the past 6 decades of data for California suggests autumn decrease is already underway. A delayed start of the rainy season of 27 days since 1960s can exacerbate seasonal droughts and prolong the wildfire season. This delay occurs due to a number of conditions that controls precipitation: the summer circulation pattern has been extending throughout November across the North Pacific, and the wintertime strengthening of the Aleutian Low is delayed. Accordingly, the southward migration of the North Pacific jet stream as well as extratropical storm tracks, which marks the start of the California rainy season, are delayed. More work, using climate models, will be needed to provide a better understanding of atmospheric conditions across the North America and the North Pacific. However, our findings provide observational evidence for the projected rainfall change over California and inform ongoing discussion about the drying/wetting tendencies of the rainy season in California.

The assessment and implementation of structural or financial instruments for climate risk mitigation requires projections of future climate risk over the operational life of each proposed instrument. A point often neglected in the climate adaptation literature is that the physical sources of predictability differ between projects with long and short planning periods: While historical and paleo climate records emphasize low‐frequency modes of variability, anthropogenic climate change is expected to alter their occurrence at longer time scales. In this paper we present a set of stylized experiments to assess the uncertainties and biases involved in estimating future climate risk over a finite future period, given a limited observational record. These experiments consider both quasi‐periodic and secular change for the underlying risk, as well as statistical models for estimating this risk from an N‐year historical record. The uncertainty of IPCC‐like future scenarios is considered through an equivalent sample size N. The relative importance of estimating short‐ or long‐term risk depends on the investment life M. Shorter design lives are preferred for situations where interannual to decadal variability can be successfully identified and predicted, highlighting the importance of sequential investment strategies for adaptation.

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

Author(s): King, AD; Perkins-Kirkpatrick, SE; Wehner, MF; Lewis, SC | Abstract: We thank the Comment's authors for their considered critique of our paper. We respond to their main criticisms and hope that this discussion motivates further consideration of communication strategies for event attribution analyses.

Arctic Ocean freshwater storage increased since the mid 1990s, but the cause was unknown. Now a recent paper by Jahn & Laiho [2020, 2020GL088854] uses ensemble runs of a coupled climate model to su...

Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE.

As global mean sea level continues to rise, thresholds corresponding to coastal inundation impacts are exceeded more frequently. This paper aims to relate sea level rise (SLR) observations and projections to their physical on‐the‐ground impacts. Using a large coastal city as an example, we show that in Sydney, Australia, frequencies of minor coastal inundation have increased from 1.6 to 7.8 days per year between 1914 and present day. We attribute over 80% of the observed coastal inundation events between 1970 and 2015 to the predominantly anthropogenic increases in global mean sea level. Further, we find that impact‐producing coastal inundation will occur weekly by 2050 under high‐ and medium‐emission/SLR scenarios and daily by 2100 under high emissions. The proportion of tide‐only coastal inundation events (i.e., where no storm surge is required to exceed flood thresholds) will increase with SLR, such that most coastal inundation events, including those considered historically severe, will become a predictable consequence of SLR and astronomical tides. These findings are important for coastal managers as frequency, severity, and predictability of inundation impacts can all now be related to the amount of SLR (e.g., a planning allowance or SLR projection). By incorporating known historical inundation events, this allows contextualization, visualization, and localization of global SLR and the changing nature of future coastal inundation risk.

Continuous structural parameterization (CSP) is a proposed method for approximating different numerical model parameterizations of the same process as functions of the same grid‐scale variables. This allows systematic comparison of parameterizations with each other and observations or resolved simulations of the same process. Using the example of two convection schemes running in the Met Office Unified Model (UM), we show that a CSP is able to capture concisely the broad behavior of the two schemes, and differences between the parameterizations and resolved convection simulated by a high resolution simulation. When the original convection schemes are replaced with their CSP emulators within the UM, basic features of the original model climate and some features of climate change are reproduced, demonstrating that CSP can capture much of the important behavior of the schemes. Our results open the possibility that future work will estimate uncertainty in model projections of climate change from estimates of uncertainty in simulation of the relevant physical processes. Plain Language Summary Numerical models are used to provide estimates of future weather and climate change. The models contain “parameterizations,” which are algorithms that simulate the effect of processes too small or poorly understood to represent using physical equations. Although they are based as much as possible on physics, parameterizations are a large source of modeling uncertainty because there can be large disagreements on how to best represent a given process. The method and even the variables used by two different parameterizations may differ. It is therefore very difficult to know how different parameterizations cause numerical models to produce different results and whether the parameterizations we have are adequate and span the range of uncertainty concerning our knowledge of the processes they represent. Using the example of small‐scale atmospheric convection linked to rain and thunderstorms, this paper describes a mathematical method for expressing different parameterizations within the same framework. This allows easy but formal mathematical comparison of different parameterizations and gives future work the potential to understand whether our parameterizations perform as they should in conjunction with future observations.

Trend turning (or trend change) is a type of structural change that is common in climate data, and methods for detecting it in time series with multiple turning‐points need to be developed. A recently developed method for this, the running slope difference (RSD) t‐test, examines trend differences in sub‐series of the sample time series to identify the trend turning‐points. In this paper, we use Monte Carlo simulation to evaluate this method's detection ability. Evaluation results show the method to be an effective tool for detecting trend turning time series and identify three major advantages of the RSD t‐test: ability to detect multiple turning‐points, capacity to detect all three types of trend turning, and great performance of reducing false alarm rate.

Flood risk to urban communities is increasing significantly as a result of the integrated effects of climate change and socioeconomic development. The latter effect is one of the main drivers of rising flood risk has received less attention in comparison to climate change. Economic development and population growth are major causes of urban expansion in flood‐prone areas, and a comprehensive understanding of the impact of urban growth on flood risk is an essential ingredient of effective flood risk management. At the same time, planning for community resilience has become a national and worldwide imperative in recent years. Enhancements to community resilience require well‐integrated and enormous long‐term public and private investments. Accordingly, comprehensive urban growth plans should take rising flood risk into account to ensure future resilient communities through careful collaboration between engineers, geologists, socialists, economists, and urban planners within the framework of life‐cycle analysis. This paper highlights the importance of including urban growth in accurate future flood risk assessment and how planning for future urbanization should include measurement science‐based strategies in developing policies to achieve more resilient communities., Key Points Urban growth, resulted by socioeconomic development, plays a crucial role on rising future flood riskUrbanization leads to changes in hazard and exposure components of risk in flood risk assessmentCombining urban expansion with the concept of community resilience and planning for future development helps to move towards a resilient community

PM2.5, or fine particulate matter, is a category of air pollutant consisting of particles with effective aerodynamic diameter equal to or less than 2.5 μm. These particles have been linked to human health impacts as well as regional haze, visibility, and climate change issues. Due to cost and space restrictions, the U.S. Environmental Protection Agency monitoring network remains spatially sparse. To increase the spatial resolution of monitoring, previous studies have used satellite data to estimate ground‐level PM concentrations, despite these estimates being associated with moderate to large uncertainties when relating a column measure of aerosol (aerosol optical depth) with surface measurements. To this end, we discuss a low‐cost air quality monitor (LCAQM) network deployed in California. In this study, we present an application of LCAQM and satellite data for quantifying the impact of wildfires in California during October 2017. The impacts of fires on PM2.5 concentration at varying temporal (hourly, daily, and weekly) and spatial (local to regional) scales have been evaluated. Comparison between low‐cost air quality sensors and reference‐grade air quality instruments shows expected performance with moderate to high uncertainties. The LCAQM measurements, in the absence of federal equivalent method data, were also found to be very useful in developing statistical models to convert aerosol optical depth into PM2.5 with performance of satellite‐derived PM2.5, similar to that obtained using the federal equivalent method data. This paper also highlights challenges associated with both LCAQM and satellite‐based PM2.5 measurements, which require further investigation and research.

The rising costs of climate change merit serious evaluation of potential climate restoration solutions. The highest rate of change in climate is observed in the Arctic where the summer ice is diminishing at an accelerated rate. The loss of Arctic sea ice increases radiative forcing and contributes to global warming. Restoring reflectivity of Arctic ice could be a powerful lever to help in the effort to limit global warming to 1.5°C. Polar ice restoration should be considered in planning of 1.5°C pathways. In this paper, a novel localized surface albedo modification technique is presented that shows promise as a method to increase multiyear ice using reflective floating materials, chosen so as to have low subsidiary environmental impact. Detailed climate modeling studying the climate impact of such a method reveals more than 1.5°C cooler temperatures over a large part of the Arctic when simulating global sea ice albedo modification. In a region north of Barents and Kara Seas temperatures have been reduced by 3°C and in North Canada by almost 1°C. Additionally, there are notable increases in sea ice thickness (20–50 cm Arctic wide) and ice concentration (>15–20% across large parts of central Arctic). These results suggest that the geoengineering technology proposed in this study may be a viable instrument for restoring Arctic ice.

Solomon Hsiang's study, which was recently published in Science, has caused extensive discussion by indicating that future climate change may exert influences on the agricultural yield, labor supply, and energy demand, among others of the United States. Based on the above study, we use an optimized input‐output model to evaluate the economic ripple effect (ERE) of the United States on the world due to climate change under Representative Concentration Pathway 4.5 with different increases in the annual mean temperature (AMT; 1, 1.5, and 2°C) between 2020 and 2100. The results show that if a loss of gross domestic product of 0.88% occurs with a 1°C AMT increase in the United States, the ERE of approximately 0.12% will be generated onto the global gross domestic product; with a 2°C increase, the ERE will triple. The time variation trend of the ERE conforms to the future variations in AMT. The degree of this effect on other regions of the world is closely related to the trade links between the United States and economic aggregates. Among these regions, Canada shows the greatest impact, followed by China. The ERE that China suffers may increase by 4.5 times as the AMT in the United States increases from 1 to 2°C. In cold regions, the benefit from global warming will decrease due to the ERE from other regions, which will inhibit their economic development. This paper aims to provide new perspectives and data‐based support for regions around the world to cope with climate change and to develop policies by studying the ERE behind international trade.