Abstract The differential warming of land and ocean leads to many continental regions in the Northern Hemisphere warming at rates higher than the global mean temperature. Adaptation and conservation efforts will, therefore, benefit from understanding regional consequences of limiting the global mean temperature increase to well below 2°C above pre-industrial levels, a limit agreed upon at the United Nations Climate Summit in Paris in December 2015. Here, we analyze climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to determine the timing and magnitude of regional temperature and precipitation changes across the contiguous United States (US) for global warming of 1.5 and 2°C and highlight consensus and uncertainties in model projections and their implications for making decisions. The regional warming rates differ considerably across the contiguous US, but all regions are projected to reach 2°C about 10-20 years before the global mean temperature. Although there is uncertainty in the timing of exactly when the 1.5 and 2°C thresholds will be crossed regionally, over 80% of the models project at least 2°C warming by 2050 for all regions for the high emissions scenario. This threshold-based approach also highlights regional variations in the rate of warming across the US. The fastest warming region in the contiguous US is the Northeast, which is projected to warm by 3°C when global warming reaches 2°C. The signal-to-noise ratio calculations indicate that the regional warming estimates remain outside the envelope of uncertainty throughout the twenty-first century, making them potentially useful to planners. The regional precipitation projections for global warming of 1.5°C and 2°C are uncertain, but the eastern US is projected to experience wetter winters and the Great Plains and the Northwest US are projected to experience drier summers in the future. The impact of different scenarios on regional precipitation projections is negligible throughout the twenty-first century compared to uncertainties associated with internal variability and model diversity.

Citation: Karmalkar AV, Bradley RS (2017) Consequences of Global Warming of 1.5 °C and 2 °C for Regional Temperature and Precipitation Changes in the Contiguous United States. PLoS ONE 12(1): e0168697. https://doi.org/10.1371/journal.pone.0168697 Editor: Juan A. Añel, Universidade de Vigo, SPAIN Received: May 18, 2016; Accepted: December 4, 2016; Published: January 11, 2017 Copyright: © 2017 Karmalkar, Bradley. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All climate model data used in this study are included as Supporting Information files and are available from the World Climate Research Programme’s CMIP5 Data portal (http://www.ipcc-data.org/sim/gcm_monthly/AR5/). Funding: The research described in this publication was supported by the U.S. Department of the Interior’s Northeast Climate Science Center, Grant or Cooperative Agreement No. G12AC00001 from the U.S. Geological Survey. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction The world leaders gathered for the 21st Conference of the Parties (COP21) in Paris in December 2015 agreed to take steps towards limiting the global mean annual surface air temperature (GMAT) increase to well below 2°C above pre-industrial levels, and to pursue efforts towards a target of 1.5°C [1]. Many studies argue that the 2°C target is overly optimistic [2, 3], since the global emissions are currently tracking the high end of plausible scenarios [4, 5] resulting in stringent limits on cumulative CO 2 emissions [6]. While there is no real scientific basis to why global warming of 2°C should be considered ‘safe’ [7], it emerged as “the least unattractive course of action” [8] and has been used as an easily understood, politically useful marker to communicate the urgency of the climate change problem and to drive action on a global scale. The inclusion of 1.5°C in the Paris Agreement, initially proposed by the small island nations, is especially pertinent to avoid the worst impacts of rising sea levels, and declining Arctic sea ice among other factors [9]. Unlike 2°C, the 1.5°C target is relatively unexplored, and studies highlighting its feasibility and impacts are beginning to emerge [10–13]. Under most low emission scenarios, global warming overshoots 1.5°C some time during the 21st century before dropping to this limit around or after 2100 [11]. As a result, 1.5°C is considered an aspirational ‘long-term’ temperature goal. The GMAT increase of 1.5 to 2°C—relevant for global scale policy recommendations and mitigation strategies—is not a helpful metric for impacts assessment and adaptation planning at regional scales. The land regions of the Northern Hemisphere are warming at rates faster than the globe [14] and are projected to cross the 2°C target before GMAT [15]. Therefore, information on regional climate projections and associated uncertainties for global warming thresholds of 1.5 and 2°C can prove beneficial to effectively communicate regional consequence of the Paris Agreement to the regional stakeholders (e.g., hydrologists, ecologists, resource planners). A study based on the Coupled Model Intercomparison Project Phase 3 (CMIP3) simulations [15] demonstrated that the contiguous United States (CONUS) is projected to experience 2°C warming during the 2040s under the A1B scenario (see Fig 4 in [15]). Their analysis focussed on regions at subcontinental scales and is inadequate to determine how the rate of warming varies across the country, which is more relevant for regional planning. The historical warming trends are different across the country with the northern and Southwest US warming at much faster rates than the Southeast and southern Great Plains [16]. Similarly, the observed precipitation has increased over CONUS in the 20th century, but with substantial seasonal and regional differences [16]. Studies of climate projections [16, 17], however, do not discuss how projected warming trends vary across the US. Also, the traditional approach of using multi-model means for a future 20-30 year period to present climate change information is not useful in communicating the timing of regional changes for specific thresholds. We use data from the CMIP5 [18] multi-model ensemble (MME) for two Representative Concentration Pathways (RCPs [19]) to compare global, national and regional projections in terms of the timing and magnitude of temperature and precipitation changes for different global warming thresholds. Specifically, we provide a regional perspective for COP21 global temperature targets by addressing the following questions: (i) when will 1.5 and 2°C warming occur regionally in the US? and (ii) what are the temperature and precipitation projections for a region when GMAT crosses 1.5 and 2°C warming thresholds? Furthermore, we also describe agreement and uncertainties in model projections.

Data and Methods The analysis is based on climate model simulations that contributed to the fifth phase of the Coupled Model Intercomparison Project (CMIP5 [18]). The CMIP5 MME used here comprises 32 models (S1 Text) and includes surface air temperature (S1 File) and precipitation data (S2 File) from historical and two RCP (RCP4.5, RCP8.5) simulations. The simulations over the historical period (1880-2005) are compared against gridded observations: Climate Research Unit Time Series (CRU TS version 3.23) data for temperature [20] and University of Delaware (UDel) data for precipitation [21]. All model data and observations were first regridded to a common 2.5°×2.5° latitude-longitude grid using bilinear interpolation to allow for comparisons to be made at same resolution. Temperature and precipitation anomalies were calculated relative to the mean of 1901-1930, the period which is common to all data sets and serves as a pre-industrial baseline for all comparisons. Note that the annual means for a given year are calculated from December of the previous year to November of that year. The methodology used to partition uncertainty in future projections is described in detail in a previous study [22] (hereafter HS09). We provide a brief summary of this method here. The total variance in temperature and precipitation projections is decomposed to quantify contributions from three different components: internal variability, model uncertainty, and scenario uncertainty. For both variables and for every model in the CMIP5 ensemble, we calculated anomaly time series from 1901 to 2099 relative to 1901-1930 mean. These time series were then smoothed using a 5-year averaging window and were fit with a fourth order polynomial. The internal variability contribution, which is assumed to be constant in time, is defined as the variance of the residuals from the least squares fits. The model uncertainty is based on variance in different model fits for each scenario, whereas the scenario uncertainty is the variance of the multi-model means for two scenarios. We assume that the performance of every model in simulating regional historical temperature and precipitation changes is equally credible. This is the only major difference in our approach and that discussed in HS09, which down-weighted models based on their historical performance while partitioning uncertainty in global mean temperature projections. The fractional uncertainty (at 90% confidence) is calculated from total uncertainty (sum of three contributions) and the ensemble mean projection across two RCP scenarios. The signal-to-noise ratio is the reciprocal of fractional uncertainty.

Conclusions and Discussion Assessing the impacts of a changing climate requires region-specific climate change information with guidance on associated uncertainties. In this study, we discuss the implications of global temperature targets set in Paris for the timing and magnitude of regional temperature and precipitation changes in the US using the CMIP5 multi-model ensemble. This threshold-based approach identifies timings associated with regional climatic changes, and clearly demonstrates how warming rates are projected to differ across the country. These aspects cannot be easily deduced from the traditional approach in which climate projections are presented in the form of a multi-model mean for a certain 20-30 year time period in the future. We also highlight consensus and disagreements in model projections, and the relative importance of different types of uncertainty in these projections as a function of time. Together, these factors provide information that can be useful to effectively communicate the consequence of the agreement reached in Paris to the regional stakeholders. All regions in the contiguous United States are projected to cross the 2°C warming threshold about 10-20 years earlier than the global mean annual temperature. While there is a large spread in TCTs across all regions, 75% to 90% of the models reach 2°C warming by 2050 for every region in the US. We believe that our estimates of TCTs based on 5-year means of annual mean temperatures may be conservative since we require that all the subsequent 5-year means beyond the identified threshold crossing time exceed the selected temperature threshold. The unpredictable nature of internal climate variability could advance or delay TCTs by a few years to a couple of decades regionally as demonstrated using the initials conditions ensemble. But this uncertainty in regional TCTs is smaller than the spread arising from using different models and two different scenarios. The consequence of large climate variability at regional scales, however, suggests that it may prove difficult to distinguish between the consequences of global warming of 1.5°C and 2°C for regional changes. Note also that the selected temperature thresholds may be temporarily crossed earlier than the identified TCTs as a result of climate variability on seasonal and interannual timescales. In fact, regional warming of 2°C relative to the baseline has already been observed for some years for all regions in the US except for the South (S4 Fig). A cooling trend in the southeast US, the so called “warming hole”, in the second half of the twentieth century was not captured by CMIP5 models [33]. Although this cooling trend has disappeared since the 1990s [34], the CMIP5 models have overestimated warming in the region over the last two decades (S4 Fig), which suggests that the actual TCTs for the South could be later than those presented here. On the other hand, the eastern US is projected to reach the 2°C target in the 2020s regardless of the scenarios. This result seems realistic given that the Paris Agreement will not come into force until 2020 [1] and that the model projections for the region match the current trends of regional warming well [35]. Importantly, the timescales associated with the eventual 1.5 and 2°C warming are long, requiring rapid development and deployment of efficient technologies throughout the 21st century [9]. Consequently, we believe that the near-term regional projections discussed here may indeed be realized regardless of what greenhouse gas concentrations pathway the world takes to meet the COP21 temperature goals. The effect of different global temperature targets on climate extremes has been a focus of a number of previous studies. The global occurrence of temperature and precipitation extremes has been shown to increase non-linearly with global mean temperature [7, 36] leading to large increases in high-impact extreme events for every degree increase in temperature. While the observed trends in the temperature extremes are within the bounds of natural variability [37], results based on CMIP3 and CMIP5 models indicate a substantial increase in summer temperature extremes in the US before global warming reaches 2°C [38]. The extreme high temperatures over CONUS are projected to increase linearly with GMAT, but at a slightly faster rate [6]. While such trends in extremes have been noted, overall, the model projections of regional climate extremes remain highly uncertain due to differing model responses and large climate variability. There are a number of caveats in the HS09 method [22] used to partition uncertainty in model projections such as the use of only two RCPs, and the constancy of internal variability estimates in time. The CMIP5 multi-model ensemble used in this study samples the structural diversity in model formulation, but was not designed for systematic exploration of uncertainties, and therefore may not span the full range of outcomes in climate projections [39]. Additionally, the use of one realization for every model is inadequate to capture the effect of internal variability that plays a significant role in driving regional changes from years to decades. While these shortcomings are not expected to affect the conclusions drawn in this work, we suggest that the uncertainty estimates be treated as reasonable guidelines. Overall, decomposing uncertainty in regional precipitation projections indicates the importance of internal variability in the short-term, model uncertainty in the long-term, and very little to no contribution of scenario uncertainty throughout the 21st century. In the absence of reliable constraints on future precipitation projections [40], assessment of regional impacts should be based on climate information obtained from a large number of models with diverse outcomes instead of putting too much emphasis on using different scenarios for a handful of models. Note that the uncertainty partitioning is relative in nature. If future generations of models reduce contributions from internal variability and model uncertainty substantially, studying precipitation response to different scenarios may become important. This, however, is unlikely given that the internal climate variations over CONUS remain large and highly unpredictable over the next 20-50 years [41, 42].

Acknowledgments This research was supported by the U.S. Department of the Interior’s Northeast Climate Science Center (NE CSC). The research described in this publication was supported by Grant or Cooperative Agreement No. G12AC00001 from the U.S. Geological Survey. Its contents are solely the responsibility of the authors and do not necessarily represent the views of the NE CSC or the USGS. This manuscript is submitted for publication with the understanding that the U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. We thank Michael Rawlins, Henry F. Diaz, and the anonymous reviewers for their constructive comments and suggestions. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author Contributions Conceptualization: AK RB. Formal analysis: AK. Investigation: AK. Methodology: AK RB. Software: AK. Validation: AK. Visualization: AK. Writing – original draft: AK. Writing – review & editing: AK RB.