scholarly journals Exploring global surface temperature pattern scaling methodologies and assumptions from a CMIP5 model ensemble

2016 ◽  
Author(s):  
Cary Lynch ◽  
Corinne Hartin ◽  
Ben Bond-Lamberty ◽  
Ben Kravitz
Keyword(s):  
Temperature Increase ◽  
Statistical Significance ◽  
Regression Method ◽  
Delta Method ◽  
Temperature Pattern ◽  
Linear Regression Method ◽  
Local Climate ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract. Pattern scaling is used to explore the uncertainty in future forcing scenarios. Of the possible techniques used for pattern scaling, the two most prominent are the delta and least squared regression methods. Both methods assume that local climate changes scale with globally averaged temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. We explore this assumption by using different time periods and scenarios, and examine the differences and the statistical significance between patterns generated by each method. Regardless of epoch chosen, the relationship between globally averaged temperature increase and local temperature are similar. Temperature patterns generated by the linear regression method show a better fit to global mean temperature change than the delta method. Differences in pat- terns between methods and epochs are largest in high latitudes (60–90 degrees N/S). Error terms in the least squared regression method are higher in lower forcing scenarios, and global mean temperature sensitivity is higher. These patterns will be used to examine feedbacks and uncertainty in the climate system.

scholarly journals An open-access CMIP5 pattern library for temperature and precipitation: Description and methodology

2017 ◽  
Author(s):  
Cary Lynch ◽  
Corinne Hartin ◽  
Ben Bond-Lamberty ◽  
Ben Kravitz
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Pattern Generation ◽  
Cmip5 Models ◽  
Linear Regression Method ◽  
High Latitudes ◽  
Global Mean Temperature ◽  
Temperature And Precipitation ◽  
Mean Temperature ◽  
Global Mean

Abstract. Pattern scaling is used to efficiently emulate general circulation models and explore uncertainty in climate projections under multiple forcing scenarios. Pattern scaling methods assume that local climate changes scale with a global mean temperature increase, allowing for spatial patterns to be generated for multiple models for any future emission scenario. Of the possible techniques used to generate patterns, the two most prominent are the delta and least squared regression methods. 5 For uncertainty quantification and probabilistic statistical analysis, a library of patterns with descriptive statistics for each file would be beneficial, but such a library does not presently exist. This paper presents patterns from all CMIP5 models for temperature and precipitation on an annual and sub-annual basis, along with the code used to generate these patterns. We explore the differences and statistical significance between patterns generated by each method and assess performance of the generated patterns across methods and scenarios. Regardless of epoch chosen, local temperature sensitivity to global mean temperature change is similar with differences of ≤ 0.2 °C. Differences in patterns across seasons between methods and epochs were largest in high latitudes (60-90° N/S). Bias and mean errors between modeled and pattern predicted output from the linear regression method were smaller than patterns generated by the delta method. Across scenarios, differences in the linear regression method patterns were more statistically significant, especially at high latitudes. We found that pattern generation methodologies were able to approximate the forced signal of change to within ≤ 0.5°C, but choice of pattern generation methodology for pattern scaling purposes should be informed by user goals and criteria. The dataset and netCDF data generation code are available at doi:10.5281/zenodo.235905.

scholarly journals Pacific variability reconciles observed and modelled global mean temperature increase since 1950

2020 ◽  
Author(s):  
Martin B. Stolpe ◽  
Kevin Cowtan ◽  
Iselin Medhaug ◽  
Reto Knutti
Keyword(s):  
Global Warming ◽  
Temperature Change ◽  
Temperature Increase ◽  
Global Temperature ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
The Pacific ◽  
Global Warming Hiatus ◽  
Warming Hiatus ◽  
Global Mean

Abstract Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean.

scholarly journals An Assessment of Global Macroeconomic Impacts Caused by Sea Level Rise Using the Framework of Shared Socioeconomic Pathways and Representative Concentration Pathways

2020 ◽  
Vol 12 (9) ◽  
pp. 3737
Author(s):  
Osamu Nishiura ◽  
Makoto Tamura ◽  
Shinichiro Fujimori ◽  
Kiyoshi Takahashi ◽  
Junya Takakura ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Low Income ◽  
Temperature Increase ◽  
General Circulation ◽  
Economic Losses ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Coastal areas provide important services and functions for social and economic activities. Damage due to sea level rise (SLR) is one of the serious problems anticipated and caused by climate change. In this study, we assess the global economic impact of inundation due to SLR by using a computable general equilibrium (CGE) model that incorporates detailed coastal damage information. The scenario analysis considers multiple general circulation models, socioeconomic assumptions, and stringency of climate change mitigation measures. We found that the global household consumption loss proportion will be 0.045%, with a range of 0.027−0.066%, in 2100. Socioeconomic assumptions cause a difference in the loss proportion of up to 0.035% without greenhouse gas (GHG) emissions mitigation, the so-called baseline scenarios. The range of the loss proportion among GHG emission scenarios is smaller than the differences among the socioeconomic assumptions. We also observed large regional variations and, in particular, the consumption losses in low-income countries are, relatively speaking, larger than those in high-income countries. These results indicate that, even if we succeed in stabilizing the global mean temperature increase below 2 °C, economic losses caused by SLR will inevitably happen to some extent, which may imply that keeping the global mean temperature increase below 1.5 °C would be worthwhile to consider.

scholarly journals Scenarios towards limiting global mean temperature increase below 1.5 °C

2018 ◽  
Vol 8 (4) ◽  
pp. 325-332 ◽  
Author(s):  
Joeri Rogelj ◽  
Alexander Popp ◽  
Katherine V. Calvin ◽  
Gunnar Luderer ◽  
Johannes Emmerling ◽  
...  
Keyword(s):  
Temperature Increase ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

scholarly journals Climate Warming in Response to Emission Reductions Consistent with the Paris Agreement

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Fang Wang ◽  
Katarzyna B. Tokarska ◽  
Jintao Zhang ◽  
Quansheng Ge ◽  
Zhixin Hao ◽  
...  
Keyword(s):  
Climate Warming ◽  
Temperature Increase ◽  
Climate Model ◽  
Ghg Emissions ◽  
Paris Agreement ◽  
The Arctic ◽  
Emission Reductions ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

To limit global warming to well below 2°C in accord with the Paris Agreement, countries throughout the world have submitted their Intended Nationally Determined Contributions (INDCs) outlining their greenhouse gas (GHG) mitigation actions in the next few decades. However, it remains unclear what the resulting climate change is in response to the proposed INDCs and subsequent emission reductions. In this study, the global and regional warming under the updated INDC scenarios was estimated from a range of comprehensive Earth system models (CMIP5) and a simpler carbon-climate model (MAGICC), based on the relationship of climate response to cumulative emissions. The global GHG emissions under the updated INDC pledges are estimated to reach 14.2∼15.0 GtC/year in 2030, resulting in a global mean temperature increase of 1.29∼1.55°C (median of 1.41°C) above the preindustrial level. By extending the INDC scenarios to 2100, global GHG emissions are estimated to be around 6.4∼9.0 GtC/year in 2100, resulting in a global mean temperature increase by 2.67∼3.74°C (median of 3.17°C). The Arctic warming is projected to be most profound, exceeding the global average by a factor of three by the end of this century. Thus, climate warming under INDC scenarios is projected to greatly exceed the long-term Paris Agreement goal of stabilizing the global mean temperature at to a low level of 1.5‐2.0°C above the pre-industrial. Our study suggests that the INDC emission commitments need to be adjusted and strengthened to bridge this warming gap.

On the scaling of climate impact indicators with global mean temperature increase: a case study of terrestrial ecosystems and water resources

2017 ◽  
Vol 141 (4) ◽  
pp. 775-782 ◽  
Author(s):  
Akemi Tanaka ◽  
Kiyoshi Takahashi ◽  
Hideo Shiogama ◽  
Naota Hanasaki ◽  
Yoshimitsu Masaki ◽  
...  
Keyword(s):  
Water Resources ◽  
Temperature Increase ◽  
Terrestrial Ecosystems ◽  
Climate Impact ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Impact Indicators ◽  
Global Mean

scholarly journals Exploring interaction effects from mechanisms between climate and land-use changes and the projected consequences on biodiversity

2021 ◽  
Vol 30 (12) ◽  
pp. 3685-3696
Author(s):  
Sarahi Nunez ◽  
Rob Alkemade
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Temperature Increase ◽  
Meta Analysis ◽  
Interaction Effects ◽  
Climate Change Effects ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

AbstractChanges in climate and land use are major drivers of biodiversity loss. These drivers likely interact and their mutual effects alter biodiversity. These interaction mechanisms are rarely considered in biodiversity assessments, as only the combined individual effects are reported. In this study, we explored interaction effects from mechanisms that potentially affect biodiversity under climate change. These mechanisms entail that climate-change effects on, for example, species abundance and species’ range shifts depend on land-use change. Similarly, land-use change impacts are contingent on climate change. We explored interaction effects from four mechanisms and projected their consequences on biodiversity. These interactions arise if species adapted to modified landscapes (e.g. cropland) differ in their sensitivity to climate change from species adapted to natural landscapes. We verified these interaction effects by performing a systematic literature review and meta-analysis of 42 bioclimatic studies (with different increases in global mean temperature) on species distributions in landscapes with varying cropland levels. We used the Fraction of Remaining Species as the effect-size metric in this meta-analysis. The influence of global mean temperature increase on FRS did not significantly change with different cropland levels. This finding excluded interaction effects between climate and landscapes that are modified by other land uses than cropping. Although we only assessed coarse climate and land-use patterns, global mean temperature increase was a good, significant model predictor for biodiversity decline. This emphasizes the need to analyse interactions between land-use and climate-change effects on biodiversity simultaneously in other modified landscapes. Such analyses should also integrate other conditions, such as spatial location, adaptive capacity and time lags. Understanding all these interaction mechanisms and other conditions will help to better project future biodiversity trends and to develop coping strategies for biodiversity conservation.

Linkage of future regional climate extremes to global warming intensity

2020 ◽  
Vol 81 ◽  
pp. 43-54
Author(s):  
X Wang ◽  
X Lang ◽  
D Jiang
Keyword(s):  
Global Warming ◽  
Temperature Increase ◽  
Regional Climate ◽  
Climate Extremes ◽  
Temperature Extremes ◽  
High Latitudes ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Low Latitudes ◽  
Global Mean

Changes in extreme climate have caused widespread concern, and it is important to understand how climate extremes will link to global warming intensity at the regional scale. Based on the daily minimum and maximum temperature and precipitation outputs from 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models under the Representative Concentration Pathways 8.5 (RCP8.5) scenario, we project the linkage of future regional climate extremes to the global mean temperature increase above preindustrial levels. Results show that regionally averaged changes in absolute temperature extremes (the coldest night and the warmest day) scale linearly with global warming intensity. In contrast, changes in cold nights and cold days in all regions, warm nights in low latitudes, and warm days in Southeast Asia exhibit nonlinear relationships with the global mean temperature increase, which manifests rapid changes in early warming stages and weak changes in late warming stages. The percentile-based temperature extremes vary at large magnitudes as global warming intensifies in low latitudes, while large values are seen in middle and high latitudes for the coldest night and warmest day, respectively; large intermodel spread occurs in the strong scaling areas, except for cold days and cold nights. Regional mean changes in extreme precipitation show consistent linear trends with global warming, and different indices vary in magnitude with region. Extreme heavy precipitation events increase linearly with global warming in high latitudes with larger magnitudes. The intermodel spread is generally large in low latitudes and will increase with warming. The work presented here can provide effective support to decision makers for developing adaptation and mitigation measures.

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