The Shape of Things to Come: Why Is Climate Change So Predictable?

Abstract The framework of feedback analysis is used to explore the controls on the shape of the probability distribution of global mean surface temperature response to climate forcing. It is shown that ocean heat uptake, which delays and damps the temperature rise, can be represented as a transient negative feedback. This transient negative feedback causes the transient climate change to have a narrower probability distribution than that of the equilibrium climate response (the climate sensitivity). In this sense, climate change is much more predictable than climate sensitivity. The width of the distribution grows gradually over time, a consequence of which is that the larger the climate change being contemplated, the greater the uncertainty is about when that change will be realized. Another consequence of this slow growth is that further efforts to constrain climate sensitivity will be of very limited value for climate projections on societally relevant time scales. Finally, it is demonstrated that the effect on climate predictability of reducing uncertainty in the atmospheric feedbacks is greater than the effect of reducing uncertainty in ocean feedbacks by the same proportion. However, at least at the global scale, the total impact of uncertainty in climate feedbacks is dwarfed by the impact of uncertainty in climate forcing, which in turn is contingent on choices made about future anthropogenic emissions.

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Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

Earth System Dynamics ◽

10.5194/esd-12-709-2021 ◽

2021 ◽

Vol 12 (2) ◽

pp. 709-723

Author(s):

Philip Goodwin ◽

B. B. Cael

Keyword(s):

Climate Sensitivity ◽

Future Climate ◽

Climate Feedback ◽

Future Climate Change ◽

Climate Projections ◽

Climate Response ◽

Ocean Heat Uptake ◽

Transient Climate Response ◽

Posterior Probability Distribution ◽

The Impact

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.

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Urban Geometry Optimization to Mitigate Climate Change: Towards Energy-Efficient Buildings

Sustainability ◽

10.3390/su13010027 ◽

2020 ◽

Vol 13 (1) ◽

pp. 27

Author(s):

Hatem Mahmoud ◽

Ayman Ragab

Keyword(s):

Climate Change ◽

Heat Stress ◽

Thermal Efficiency ◽

Energy Demand ◽

Building Blocks ◽

Climate Projections ◽

Climate Change Scenarios ◽

Urban Geometry ◽

Outdoor Spaces ◽

The Impact

The density of building blocks and insufficient greenery in cities tend to contribute dramatically not only to increased heat stress in the built environment but also to higher energy demand for cooling. Urban planners should, therefore, be conscious of their responsibility to reduce energy usage of buildings along with improving outdoor thermal efficiency. This study examines the impact of numerous proposed urban geometry cases on the thermal efficiency of outer spaces as well as the energy consumption of adjacent buildings under various climate change scenarios as representative concentration pathways (RCP) 4.5 and 8.5 climate projections for New Aswan city in 2035. The investigation was performed at one of the most underutilized outdoor spaces on the new campus of Aswan University in New Aswan city. The potential reduction of heat stress was investigated so as to improve the thermal comfort of the investigated outdoor spaces, as well as energy savings based on the proposed strategies. Accordingly, the most appropriate scenario to be adopted to cope with the inevitable climate change was identified. The proposed scenarios were divided into four categories of parameters. In the first category, shelters partially (25–50% and 75%) covering the streets were used. The second category proposed dividing the space parallel or perpendicular to the existing buildings. The third category was a hybrid scenario of the first and second categories. In the fourth category, a green cover of grass was added. A coupling evaluation was applied utilizing ENVI-met v4.2 and Design-Builder v4.5 to measure and improve the thermal efficiency of the outdoor space and reduce the cooling energy. The results demonstrated that it is better to cover outdoor spaces with 50% of the overall area than transform outdoor spaces into canyons.

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Soil Type Modifies the Impacts of Warming and Snow Exclusion on Carbon and Nutrient Losses

10.21203/rs.3.rs-1139295/v1 ◽

2021 ◽

Author(s):

Stephanie M. Juice ◽

Paul G. Schaberg ◽

Alexandra M. Kosiba ◽

Carl E. Waite ◽

Gary J. Hawley ◽

...

Keyword(s):

Climate Change ◽

Nutrient Cycling ◽

Soil Type ◽

Soil Characteristics ◽

Nutrient Loss ◽

Climate Projections ◽

Nutrient Losses ◽

N Losses ◽

Total N ◽

The Impact

Abstract The varied and wide-reaching impacts of climate change are occurring across heterogeneous landscapes. Despite the known importance of soils in mediating biogeochemical nutrient cycling, there is little experimental evidence of how soil characteristics may shape ecosystem response to climate change. Our objective was to clarify how soil characteristics modify the impact of climate changes on carbon and nutrient leaching losses in temperate forests. We therefore conducted a field-based mesocosm experiment with replicated warming and snow exclusion treatments on two soils in large (2.4 m diameter), in-field forest sapling mesocosms. We found that nutrient loss responses to warming and snow exclusion treatments frequently varied substantially by soil type. Indeed, in some cases, soil type nullified the impact of a climate treatment. For example, warming and snow exclusion increased nitrogen (N) losses on fine soils by up to four times versus controls, but these treatments had no impact on coarse soils. Generally, the coarse textured soil, with its lower soil-water holding capacity, had higher nutrient losses (e.g., 12-17 times more total N loss from coarse than fine soils), except in the case of phosphate, which had consistently higher losses (23-58%) from the finer textured soil. Furthermore, the mitigation of nutrient loss by increasing tree biomass varied by soil type and nutrient. Our results suggest that potentially large biogeochemical responses to climate change are strongly mediated by soil characteristics, providing further evidence of the need to consider soil properties in Earth system models for improving nutrient cycling and climate projections.

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Structural safety and design under climate change

IABSE Congress, New York, New York 2019: The Evolving Metropolis ◽

10.2749/newyork.2019.1129 ◽

2019 ◽

Author(s):

Pietro Croce ◽

Paolo Formichi ◽

Filippo Landi

Keyword(s):

Climate Change ◽

Greenhouse Gas ◽

Structural Reliability ◽

Greenhouse Gas Emission ◽

Extreme Value ◽

Structural Safety ◽

Climate Projections ◽

Climate Conditions ◽

Impact Of Climate Change ◽

The Impact

The impact of climate change on climatic actions could significantly affect, in the mid-term future, the design of new structures as well as the reliability of existing ones designed in accordance to the provisions of present and past codes. Indeed, current climatic loads are defined under the assumption of stationary climate conditions but climate is not stationary and the current accelerated rate of changes imposes to consider its effects.Increase of greenhouse gas emissions generally induces a global increase of the average temperature, but at local scale, the consequences of this phenomenon could be much more complex and even apparently not coherent with the global trend of main climatic parameters, like for example, temperature, rainfalls, snowfalls and wind velocity.In the paper, a general methodology is presented, aiming to evaluate the impact of climate change on structural design, as the result of variations of characteristic values of the most relevant climatic actions over time. The proposed procedure is based on the analysis of an ensemble of climate projections provided according a medium and a high greenhouse gas emission scenario. Factor of change for extreme value distribution’s parameters and return values are thus estimated in subsequent time windows providing guidance for adaptation of the current definition of structural loads.The methodology is illustrated together with the outcomes obtained for snow, wind and thermal actions in Italy. Finally, starting from the estimated changes in extreme value parameters, the influence on the long-term structural reliability can be investigated comparing the resulting time dependent reliability with the reference reliability levels adopted in modern Structural codes.

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Equilibrium Spin-up of Cold and Warm Permafrost Models

10.5194/egusphere-egu21-13922 ◽

2021 ◽

Author(s):

Cameron Ross ◽

Ryley Beddoe ◽

Greg Siemens

Keyword(s):

Climate Change ◽

Temperature Response ◽

Ground Temperature ◽

Future Climate Change ◽

Climate Projections ◽

Multiple Model ◽

Climate Data ◽

Ground Temperatures ◽

C Cycle ◽

Warm Permafrost

Initialization (spin-up) of a numerical ground temperature model is a critical but often neglected step for solving heat transfer problems in permafrost. Improper initialization can lead to significant underlying model drift in subsequent transient simulations, distorting the effects on ground temperature from future climate change or applied infrastructure. &#160;In a typical spin-up simulation, a year or more of climate data are applied at the surface and cycled repeatedly until ground temperatures are declared to be at equilibrium with the imposed boundary conditions, and independent of the starting conditions.Spin-up equilibrium is often simply declared after a specified number of spin-up cycles. In few studies, equilibrium is visually confirmed by plotting ground temperatures vs spin-up cycles until temperatures stabilize; or is declared when a certain inter-cycle-temperature-change threshold is met simultaneously at all depths, such as &#8710;T &#8804; 0.01oC per cycle. In this study, we investigate the effectiveness of these methods for determining an equilibrium state in a variety of permafrost models, including shallow and deep (10 &#8211; 200 m), high and low saturation soils (S = 100 and S = 20), and cold and warm permafrost (MAGT = ~-10 oC and >-1 oC). The efficacy of equilibrium criteria 0.01oC/cycle and 0.0001oC/cycle are compared. Both methods are shown to prematurely indicate equilibrium in multiple model scenarios. &#160;Results show that no single criterion can programmatically detect equilibrium in all tested models, and in some scenarios can result in up to 10oC temperature error or 80% less permafrost than at true equilibrium. &#160;A combination of equilibrium criteria and visual confirmation plots is recommended for evaluating and declaring equilibrium in a spin-up simulation.Long-duration spin-up is particularly important for deep (10+&#160;m) ground models where thermal inertia of underlying permafrost slows the ground temperature response to surface forcing, often requiring hundreds or even thousands of spin-up cycles to establish equilibrium. Subsequent transient analyses also show that use of a properly initialized 100 m permafrost model can reduce the effect of climate change on mean annual ground temperature of cold permafrost by more than 1 oC and 3 oC under RCP2.6 and RCP8.5 climate projections, respectively, when compared to an identical 25 m model. These results have important implications for scientists, engineers and policy makers that rely on model projections of long-term permafrost conditions.

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Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

Geoscientific Model Development ◽

10.5194/gmd-4-1103-2011 ◽

2011 ◽

Vol 4 (4) ◽

pp. 1103-1114 ◽

Cited By ~ 36

Author(s):

F. Maignan ◽

F.-M. Bréon ◽

F. Chevallier ◽

N. Viovy ◽

P. Ciais ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Vegetation Index ◽

Vegetation Indices ◽

Global Scale ◽

Vegetation Dynamic ◽

Rate Of Increase ◽

Vegetation Models ◽

Global Vegetation ◽

The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

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What role will climate change play in EU agricultural markets? An integrated assessment taking into account carbon fertilization effects

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2017153-9899 ◽

2017 ◽

Vol 15 (3) ◽

pp. e0115 ◽

Cited By ~ 2

Author(s):

Pilar Martinez ◽

María Blanco ◽

Benjamin Van Doorslaer ◽

Fabien Ramos ◽

Andrej Ceglar

Keyword(s):

Climate Change ◽

Supply And Demand ◽

Agricultural Markets ◽

Climate Projections ◽

Baseline Scenario ◽

Market Uncertainty ◽

Agricultural Market ◽

Carbon Fertilization ◽

Fertilization Effects ◽

The Impact

Recent studies point to climate change being one of the long-term drivers of agricultural market uncertainty. To advance in the understanding of the influence of climate change on future agricultural market developments, we compared a baseline scenario for the year 2030 with alternative simulation scenarios that differ regarding: (1) emission scenarios; (2) climate projections; and (3) the consideration of carbon fertilization effects on crop growth. For each simulation scenario, the CAPRI model provides global and EU-wide impacts of climate change on agricultural markets. Results showed that climate change would considerably affect agrifood markets up to 2030. Nevertheless, market-driven adaptation strategies (production intensification, trade adjustments) would soften the impact of yield shocks on supply and demand. As a result, regional changes in production would be lower than foreseen by other studies focused on supply effects.

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Climate Perspectives in the Intra–Americas Seas

Atmosphere ◽

10.3390/atmos11090959 ◽

2020 ◽

Vol 11 (9) ◽

pp. 959

Author(s):

Ana María Durán-Quesada ◽

Rogert Sorí ◽

Paulina Ordoñez ◽

Luis Gimeno

Keyword(s):

Climate Change ◽

Extreme Events ◽

Large Scale ◽

Negative Impact ◽

Current Knowledge ◽

Regional Climate ◽

Climate Projections ◽

Economic Activities ◽

Weather And Climate ◽

The Impact

The Intra–Americas Seas region is known for its relevance to air–sea interaction processes, the contrast between large water masses and a relatively small continental area, and the occurrence of extreme events. The differing weather systems and the influence of variability at different spatio–temporal scales is a characteristic feature of the region. The impact of hydro–meteorological extreme events has played a huge importance for regional livelihood, having a mostly negative impact on socioeconomics. The frequency and intensity of heavy rainfall events and droughts are often discussed in terms of their impact on economic activities and access to water. Furthermore, future climate projections suggest that warming scenarios are likely to increase the frequency and intensity of extreme events, which poses a major threat to vulnerable communities. In a region where the economy is largely dependent on agriculture and the population is exposed to the impact of extremes, understanding the climate system is key to informed policymaking and management plans. A wealth of knowledge has been published on regional weather and climate, with a majority of studies focusing on specific components of the system. This study aims to provide an integral overview of regional weather and climate suitable for a wider community. Following the presentation of the general features of the region, a large scale is introduced outlining the main structures that affect regional climate. The most relevant climate features are briefly described, focusing on sea surface temperature, low–level circulation, and rainfall patterns. The impact of climate variability at the intra–seasonal, inter–annual, decadal, and multi–decadal scales is discussed. Climate change is considered in the regional context, based on current knowledge for natural and anthropogenic climate change. The present challenges in regional weather and climate studies have also been included in the concluding sections of this review. The overarching aim of this work is to leverage information that may be transferred efficiently to support decision–making processes and provide a solid foundation on regional weather and climate for professionals from different backgrounds.

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Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽

10.3390/cli8120139 ◽

2020 ◽

Vol 8 (12) ◽

pp. 139

Author(s):

Manashi Paul ◽

Sijal Dangol ◽

Vitaly Kholodovsky ◽

Amy R. Sapkota ◽

Masoud Negahban-Azar ◽

...

Keyword(s):

Climate Change ◽

Crop Yield ◽

Crop Yields ◽

Swat Model ◽

Global Climate Models ◽

Climate Projections ◽

Climate Change Scenarios ◽

River Watershed ◽

Temperature And Precipitation ◽

The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

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Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽

10.3390/w11112266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2266 ◽

Cited By ~ 4

Author(s):

Enrique Soriano ◽

Luis Mediero ◽

Carlos Garijo

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Control Period ◽

Flood Frequency ◽

Climate Projections ◽

The Future ◽

Flood Quantiles ◽

The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

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