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Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by...

Solar Radiation Management (SRM) and Carbon Dioxide Removal (CDR) have been proposed to mitigate global warming in the event of insufficient greenhouse gas emission reductions. We have studied temperature and precipitation responses to CDR and SRM with the RCP4.5 scenario using the MPI-ESM and CESM Earth System Models (ESMs). The two SRM scenarios were designed to meet different climate targets to keep either global mean 1) surface temperature or 2) precipitation at the 2010-2020 level via stratospheric sulfur injections. This was done in two-fold method, where global aerosol fields were first simulated with aerosol-climate model ECHAM-HAMMOZ, which were then used as prescribed fields in ESM simulations. In the CDR scenario the annual CO2 increase based on RCP4.5 was counteracted by a 1% annual removal of the atmospheric CO2 concentration which decreased the global mean temperature back to the 2010-2020 level at the end of this century. Results showed that applying SRM to offset 21st century climate warming in the RCP4.5 scenario led to a 1.42% (MPI-ESM) or 0.73% (CESM) reduction in global mean precipitation, whereas CDR increased global precipitation by 0.5% in both ESMs for 2080-2100 relative to 2010-2020. To study this further we separated global precipitation responses to a temperature-dependent and a fast temperature-independent components. These were quantified by a regression method. In this method the climate variable (e.g. precipitation) is regressed against the temperature change due to the instantaneous forcing. Temperature-dependent slow response and temperature independent fast response are given by the fitted regression line. We showed that in all simulated geoengineering scenarios, the simulated global mean precipitation change can be represented as the sum of these response components. This component analysis shows that the fast temperature-independent component of atmospheric CO2 concentration explains the global mean precipitation change in both SRM and CDR scenarios. Results showed relatively large differences in the individual precipitation components between two ESMs. This component analysis method can be generalized to evaluate and analyze precipitation, or other climate responses, basically in any emission scenario and in any ESM in a conceptually easy way. Based on the SRM simulations, a total of or 292-318 Tg(S) (MPI-ESM) or 163-199 Tg(S) (CESM) of injected sulfur from 2020 to 2100 was required to offset global mean warming based on the RCP4.5 scenario. The distinct effects of SRM in the two ESM simulations mainly reflected differing shortwave absorption responses to water vapor. To prevent a global mean precipitation increase, only 95-114 Tg(S) was needed. Simultaneously this prevent the global mean climate warming from exceeding 2 degrees above preindustrial temperatures in both models.

This study demonstrates the relationship between the Pacific–North American (PNA) teleconnection pattern and the Great Plains low-level jet (GPLLJ). The negative phase of the PNA, which is associated with lower heights over the Great Plains and ridging in the southeastern United States, enhances the GPLLJ by increasing the pressure gradient within the GPLLJ on 6-hourly to monthly time scales. Strong GPLLJ events predominantly occur when the PNA is negative. Warm-season strong GPLLJ events with a very negative PNA (

Data from a dense urban meteorological network (UMN) are analyzed, revealing the spatial heterogeneity and temporal variability of the Twin Cities (Minneapolis–St. Paul, Minnesota) canopy-layer urban heat island (UHI). Data from individual sensors represent surface air temperature (SAT) across a variety of local climate zones within a 5000-km2 area and span the 3-yr period from 1 August 2011 to 1 August 2014. Irregularly spaced data are interpolated to a uniform 1 km × 1 km grid using two statistical methods: 1) kriging and 2) cokriging with impervious surface area data. The cokriged SAT field exhibits lower bias and lower RMSE than does the kriged SAT field when evaluated against an independent set of observations. Maps, time series, and statistics that are based on the cokriged field are presented to describe the spatial structure and magnitude of the Twin Cities metropolitan area (TCMA) UHI on hourly, daily, and seasonal time scales. The average diurnal variation of the TCMA UHI exhibits distinct seasonal modulation wherein the daily maximum occurs by night during summer and by day during winter. Daily variations in the UHI magnitude are linked to changes in weather patterns. Seasonal variations in the UHI magnitude are discussed in terms of land–atmosphere interactions. To the extent that they more fully resolve the spatial structure of the UHI, dense UMNs are advantageous relative to limited collections of existing urban meteorological observations. Dense UMNs are thus capable of providing valuable information for UHI monitoring and for implementing and evaluating UHI mitigation efforts.

The frequency and intensity of heavy rainfall events have increased in the Central U.S. over the last several decades, and model projections from dynamical downscaling suggest a continuation with climate change. In this study, we examine how climate change might affect mechanisms related to the development of heavy rainfall events that occur on the scale of mesoscale convective systems over the Central U.S. To accomplish these goals, we incorporate dynamical downscaled simulations of two Coupled Model Intercomparison Project phase 5 models in the Weather Research and Forecasting model that accurately simulate heavy rainfall events. For each model, a set of heavy rainfall events that match the frequency, timing, and intensity of observed events are objectively identified in historical and future simulations. We then examine multimodel composites of select atmospheric fields during these events in simulations of historical and future scenarios, enabling an identification of possible physical mechanisms that could contribute to the intensification of heavy rainfall events with climate change. Simulations show that additional moisture is transported into convective updrafts during heavy rain events in future simulations, driving stronger evaporative cooling from the entrainment of drier midtropospheric air. This results in the formation of a stronger low-level cold pool, which enhances moisture convergence above the cold pool and increases rainfall rates during future heavy precipitation events. In addition, a warmer profile in future simulations might allow for heavier rainfall rates as a deeper atmospheric column can support additional collision-coalescence of liquid hydrometeors.

A new approach is used to detect atmospheric teleconnections without being bound by orthogonality (such as empirical orthogonal functions). This method employs negative correlations in a global dataset to detect potential teleconnections. One teleconnection occurs between the Tasman Sea and the Southern Ocean. It is related to El Niño–Southern Oscillation (ENSO), the Indian Ocean dipole (IOD), and the southern annular mode (SAM). This teleconnection is significantly correlated with SAM during austral summer, fall, and winter, with IOD during spring, and with ENSO in summer. It can thus be described as a hybrid between these modes. Given previously found relationships between IOD and ENSO, and IOD’s proximity to the teleconnection centers, correlations to IOD are generally stronger than to ENSO. Increasing pressure over the Tasman Sea leads to higher (lower) surface temperature over eastern Australia (the southwestern Pacific) in all seasons and is related to reduced surface temperature over Wilkes Land and Adélie Land in Antarctica during fall and winter. Precipitation responses are generally negative over New Zealand. For one standard deviation of the teleconnection index, precipitation anomalies are positive over Australia in fall, negative over southern Australia in winter and spring, and negative over eastern Australia in summer. When doubling the threshold, the size of the anomalous high-pressure center increases and annual precipitation anomalies are negative over southeastern Australia and northern New Zealand. Eliassen–Palm fluxes quantify the seasonal dependence of SAM, ENSO, and IOD influences. Analysis of the dynamical interactions between these teleconnection patterns can improve prediction of seasonal temperature and precipitation patterns in Australia and New Zealand.

Over the last century, the Arctic has warmed at twice the rate of the planet as a whole. Observational evidence indicates that this rapid warming is affecting the tundra and boreal forest biomes by changing their structure and geographic distribution. A global climate model (GCM) was used to explore the atmospheric response to boreal forest expansion by applying a one-grid cell shift of the forest into tundra. This subtle shift is meant to represent the expansion that would occur this century rather than more extreme scenarios predicted by dynamic vegetation models. Results show that this shift causes an average annual warming of 0.3 °C over the region because of a reduction in the surface albedo and an increase in net radiation. A warming of ~1.0 °C occurs in spring when the forest masks the higher albedo snow-covered surface and results in snowmelt and a reduction in cloud cover. Results fail to show a larger-scale dynamical response although some warming of the lower and mid troposphere occurs in July. No changes were found in the position or strength of the Arctic frontal zone as some studies have indicated will occur with a shift in the boreal forest-tundra boundary. These findings suggest that coupled model simulations that predict larger changes in vegetation distribution are likely overemphasizing the amount of Arctic warming that will occur this century. These findings also indicate that a realistic dynamical response to subtle land cover change might not be correctly simulated by GCMs run at coarse spatial resolutions.

Since World War II, the expansion of irrigation throughout the Great Plains has resulted in a significant decline in the water table of the Ogallala Aquifer, threatening its long-term sustainability. The addition of near-surface water for irrigation has previously been shown to impact the surface energy and water budgets by modifying the partitioning of latent and sensible heating. A strong increase in latent heating drives near-surface cooling and an increase in humidity, which has opposing impacts on convective precipitation. In this study, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation on precipitation. Using a satellite-derived fractional irrigation dataset, grid cells were divided into irrigated and nonirrigated segments and the near-surface soil layer within irrigated segments was held at saturation. Nine April–October periods (three drought, three normal, and three pluvial) were simulated over the Great Plains. Averaging over all simulations, May–September precipitation increased by 4.97 mm (0.91%), with localized increases of up to 20%. The largest precipitation increases occurred during pluvial years (6.14 mm; 0.98%) and the smallest increases occurred during drought years (2.85 mm; 0.63%). Precipitation increased by 7.86 mm (1.61%) over irrigated areas from the enhancement of elevated nocturnal convection. Significant precipitation increases occurred over irrigated areas during normal and pluvial years, with decreases during drought years. This suggests that a soil moisture threshold likely exists whereby irrigation suppresses convection over irrigated areas when soil moisture is extremely low and enhances convection when antecedent soil moisture is relatively high.

This study combines previous work on quantifying the greenhouse gas value of ecosystems with models of the effects of biophysical processes to produce an integrated metric of climate-regulation services. The approach is used to quantify climate-regulation values of natural and managed ecosystems across the Western Hemisphere.

Over the last 100 years, Arctic warming has resulted in a longer growing season in boreal and tundra ecosystems. This has contributed to a slow northward expansion of the boreal forest and a decrease in the surface albedo. Corresponding changes to the surface and atmospheric energy budgets have contributed to a broad region of warming over areas of boreal forest expansion. In addition, mesoscale and synoptic scale patterns have changed as a result of the excess energy at and near the surface. Previous studies have identified a relationship between the positioning of the boreal forest-tundra ecotone and the Arctic frontal zone in summer. This study examines the climate response to hypothetical boreal forest expansion and its influence on the summer Arctic frontal zone. Using the Weather Research and Forecasting model over the Northern Hemisphere, an experiment was performed to evaluate the atmospheric response to expansion of evergreen and deciduous boreal needleleaf forests into open shrubland along the northern boundary of the existing forest. Results show that the lower surface albedo with forest expansion leads to a local increase in net radiation and an average hemispheric warming of 0.6°C at and near the surface during June with some locations warming by 1–2°C. This warming contributes to changes in the meridional temperature gradient that enhances the Arctic frontal zone and strengthens the summertime jet. This experiment suggests that continued Northern Hemisphere high-latitude warming and boreal forest expansion might contribute to additional climate changes during the summer.

Numerous studies have identified the regional-scale climate response to tropical deforestation through changes to water, energy, and momentum fluxes between the land surface and the atmosphere. There has been little research, however, on the role of tropical deforestation on the global climate. Previous studies have focused on the climate response in the extratropics with little analysis of the mechanisms responsible for propagating the signal out of the tropics. A climate modeling study is presented of the physical processes that are important in transmitting a deforestation signal out of the tropics to the Northern Hemisphere extratropics in boreal winter. Using the Community Climate System Model, version 3 Integrated Biosphere Simulator (CCM3–IBIS) climate model and by imposing an exaggerated land surface forcing of complete tropical forest removal, the thermodynamic and dynamical atmospheric response is evaluated regionally within the tropics, globally as the climate signal propagates to the Northern Hemisphere, and then regionally in Eurasia where land–atmosphere feedbacks contribute to amplifying the climate signal and warming the surface and lower troposphere by 1–4 K. Model results indicate that removal of the tropical forests causes weakening of deep tropical convection that excites a Rossby wave train emanating northeastward away from the South American continent. Changes in European storm-track activity cause an intensification and northward shift in the Ferrel cell that leads to anomalous adiabatic warming over a broad region of Eurasia. Regional-scale land–atmosphere feedbacks are found to amplify the warming. While hypothetical, this approach illustrates the atmospheric mechanisms linking the tropics with Eurasia that may otherwise not be detectable with more realistic land-use change simulations.

New approach proposed for defining preconditions for human development Crossing certain biophysical thresholds could have disastrous consequences for humanity Three of nine interlinked planetary boundaries have already been overstepped

The Amazon Basin is one of the world's most important bioregions, harboring a rich array of plant and animal species and offering a wealth of goods and services to society. For years, ecological science has shown how large-scale forest clearings cause declines in biodiversity and the availability of forest products. Yet some important changes in the rainforests, and in the ecosystem services they provide, have been underappreciated until recently. Emerging research indicates that land use in the Amazon goes far beyond clearing large areas of forest; selective logging and other canopy damage is much more pervasive than once believed. Deforestation causes collateral damage to the surrounding forests – through enhanced drying of the forest floor, increased frequency of fires, and lowered productivity. The loss of healthy forests can degrade key ecosystem services, such as carbon storage in biomass and soils, the regulation of water balance and river flow, the modulation of regional climate patterns, and the ...

Agriculture has significantly transformed the face of the planet. In particular, croplands have replaced natural vegetation over large areas of the global land surface, covering around 18 million km2 of the land surface today. To grow crops, humans have taken advantage of the resource provided by climate — optimum temperature and precipitation. However, the clearing of land for establishing croplands might have resulted in an inadvertent change in the climate. This feedback might, in turn, have altered the suitability of land for growing crops. In this sensitivity study, we used a combination of land cover data sets, numerical models, and cropland suitability analysis, to estimate the degree to which the replacement of natural vegetation by croplands might have altered the land suitability for cultivation. We found that the global changes in cropland suitability are likely to have been fairly small, however large regional changes in cropland suitability might have occurred. Our theoretical study showed that major changes in suitability occurred in Canada, Eastern Europe, the Former Soviet Union, northern India, and China. Although the magnitude, sign, and spatial patterns of change indicated by this study may be an artifact of our particular model and experimental design, our study is illustrative of the potential inadvertent consequences of human activities on the land. Moreover, it offers a methodology for evaluating how climate changes due to human activities on the land may alter the multiple services offered by ecosystems to human beings.

The participation of different vegetation types within the physical climate system is investigated using a coupled atmosphere-biosphere model, CCM3-IBIS. We analyze the effects that six different vegetation biomes (tropical, boreal, and temperate forests, savanna, grassland and steppe, and shrubland/tundra) have on the climate through their role in modulating the biophysical exchanges of energy, water, and momentum between the land-surface and the atmosphere. Using CCM3-IBIS we completely remove the vegetation cover of a particular biome and compare it to a control simulation where the biome is present, thereby isolating the climatic effects of each biome. Results from the tropical and boreal forest removal simulations are in agreement with previous studies while the other simulations provide new evidence as to their contribution in forcing the climate. Removal of the temperate forest vegetation exhibits behavior characteristic of both the tropical and boreal simulations with cooling during winter and spring due to an increase in the surface albedo and warming during the summer caused by a reduction in latent cooling. Removal of the savanna vegetation exhibits behavior much like the tropical forest simulation while removal of the grassland and steppe vegetation has the largest effect over the central United States with warming and drying of the atmosphere in summer. The largest climatic effect of shrubland and tundra vegetation removal occurs in DJF in Australia and central Siberia and is due to reduced latent cooling and enhanced cold air advection, respectively. Our results show that removal of the boreal forest yields the largest temperature signal globally when either including or excluding the areas of forest removal. Globally, precipitation is most affected by removal of the savanna vegetation when including the areas of vegetation removal, while removal of the tropical forest most influences the global precipitation excluding the areas of vegetation removal.

While the earth's climate can affect the structure and functioning of terrestrial ecosystems, the process also works in reverse. As a result, changes in terrestrial ecosystems may influence climate through both biophysical and biogeochemical processes. This two-way link between the physical climate system and the biosphere is under increasing scrutiny. We review recent developments in the analysis of this interaction, focusing in particular on how alterations in the structure and functioning of terrestrial ecosystems, through either human land-use practices or global climate change, may affect the future of the earth's climate.

Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

Climate change is expected to increase the frequency of hydrological extremes, producing more droughts and heavy rainfall events globally. How warm-season precipitation extremes will change over the Central U.S. is unclear because most coarse spatial resolution global climate models inadequately simulate hydrological extremes resulting from convective precipitation. However, the higher spatial resolution from dynamical downscaling potentially enables improved projections of future changes in extreme rainfall events. In this study, we downscaled two models from the Coupled Model Intercomparison Project-Phase 5 (CMIP5) using the Weather Research and Forecasting model for one historical period (1990–1999), two future periods (2040–2049, 2090–2099) in a midrange emissions scenario (Representative Concentration Pathway (RCP) 4.5), and one period (2090–2099) in a high emissions (RCP8.5) scenario. The diurnal cycle, extremes, and averages of precipitation in historical simulations compare well with observations. While the future change in the total amount of precipitation is unclear, model simulations suggest that summer rainfall will be less frequent, but more intense when precipitation does occur. Significant intensification of the heaviest rainfall events occurs in the models, with the greatest changes in the early warm season (April). Increases in total April–July rainfall and the enhancement of extreme rainfall events in the RCP8.5 2090s are related to a stronger Great Plains Low-Level Jet (GPLLJ) during those months. Conversely, late warm-season drying over the North Central U.S. is present in nearly all future simulations, with increased drought in August–September associated with a slight weakening of the GPLLJ. Simulated trends generally increase with stronger greenhouse gas forcing.

Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet's resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

[1] Despite supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.

[1] Previous studies have indicated how tropical deforestation can have a significant influence on regional and global climate through altered biophysical exchanges of water, energy, and momentum at the land-atmosphere boundary. However, the mechanisms for translating a surface forcing to changes in the atmospheric thermodynamics and circulation have not received as much attention. Here we present a new moist static energy budget method for examining the regional atmospheric response to removal of tropical forests and how land surface forcing is propagated into the atmosphere. A detailed three-dimensional grid cell energy budget approach is used within a coupled atmosphere-biosphere model (Community Climate Model, Version 3–Integrated Biosphere Simulator (CCM3-IBIS)) to identify how land surface forcing affects the regional climate through the vertical and horizontal movement of moist static energy. This approach allows us to clearly identify where the moist static energy budget changes, which mechanisms are responsible for the changes, and how energy moves to adjacent areas and affects rainfall. Generally, replacement of the tropical forests with bare soil in the model leads to decreased rainfall in the tropics due to regional drying, while enhanced rainfall occurs in the subtropics associated with strengthened monsoon winds importing more moisture. Interesting regional complexities emerge, notably in tropical Africa. There, removal of the forests leads to lower rainfall near the coast but enhanced rainfall in central tropical Africa. This approach provides a useful diagnostic tool for examining the implications of land use and land cover change on the regional and global atmospheric thermodynamics and circulation.

"Anthropogenic pressures on the Earth System have reached a scale where abrupt global environmental change can no longer be excluded. We propose a new approach to global sustainability in which we define planetary boundaries within which we expect that humanity can operate safely. Transgressing one or more planetary boundaries may be deleterious or even catastrophic due to the risk of crossing thresholds that will trigger non-linear, abrupt environmental change within continental- to planetary-scale systems. We have identified nine planetary boundaries and, drawing upon current scientific understanding, we propose quantifications for seven of them. These seven are climate change (CO2 concentration in the atmosphere