Persistent high latitude amplification over the past 10 million years

Author(s):  
Xiaoqing Liu ◽  
Matthew Huber ◽  
Gavin L Foster ◽  
R Mark Leckie ◽  
Yi Ge Zhang

<p>When the Earth warms, the high latitudes often warm more than the low latitudes, a phenomenon commonly known as high latitude amplification. Although high latitude amplification has been observed by both climate data and models, the trajectory of high latitude amplification in our future changing climate is uncertain. Pacific-wide reconstructions of sea surface temperature variability from past climates are important for establishing the historical records of high latitude amplification. Multiple extratropical temperature records have been established for the past 10 million years (Myr). However, it is debated whether the warmest end member, the Western Pacific Warm Pool (WPWP), warmed during the late Miocene (~12 to 5 million years ago, Ma) and Pliocene (5 to 3 Ma). Here we present new multi-proxy, multi-site paleotemperature records from the WPWP. These results, based on lipid biomarkers and foraminiferal Mg/Ca, unequivocally show warmer temperatures in the past, and a secular cooling over the last 10 Myr. We combine these new data, along with the previously established paleotemperature records, to reveal a persistent pattern of change in the Pacific described by a high latitude amplification factor of ~1.7, which does not seem to be affected by the major climate changes over the past 10 Myr. The evolution of spatial temperature gradients in the Pacific is also evident in climate model output and instrumental observations covering the last 160 years, and thus appears to be a robust and predictable feature of the climate system. These results therefore confirm that climate models can capture the major features of past climate change, providing increased confidence in their predictions of future patterns that are likely to be similar to those reconstructed here.</p>

2018 ◽  
Vol 11 (6) ◽  
pp. 2033-2048 ◽  
Author(s):  
Richard Hyde ◽  
Ryan Hossaini ◽  
Amber A. Leeson

Abstract. Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to ∼ 20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at ∼ 62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models.


2016 ◽  
Vol 2 (7) ◽  
pp. e1501719 ◽  
Author(s):  
Evan Weller ◽  
Seung-Ki Min ◽  
Wenju Cai ◽  
Francis W. Zwiers ◽  
Yeon-Hee Kim ◽  
...  

The Indo-Pacific warm pool (IPWP) has warmed and grown substantially during the past century. The IPWP is Earth’s largest region of warm sea surface temperatures (SSTs), has the highest rainfall, and is fundamental to global atmospheric circulation and hydrological cycle. The region has also experienced the world’s highest rates of sea-level rise in recent decades, indicating large increases in ocean heat content and leading to substantial impacts on small island states in the region. Previous studies have considered mechanisms for the basin-scale ocean warming, but not the causes of the observed IPWP expansion, where expansion in the Indian Ocean has far exceeded that in the Pacific Ocean. We identify human and natural contributions to the observed IPWP changes since the 1950s by comparing observations with climate model simulations using an optimal fingerprinting technique. Greenhouse gas forcing is found to be the dominant cause of the observed increases in IPWP intensity and size, whereas natural fluctuations associated with the Pacific Decadal Oscillation have played a smaller yet significant role. Further, we show that the shape and impact of human-induced IPWP growth could be asymmetric between the Indian and Pacific basins, the causes of which remain uncertain. Human-induced changes in the IPWP have important implications for understanding and projecting related changes in monsoonal rainfall, and frequency or intensity of tropical storms, which have profound socioeconomic consequences.


2021 ◽  
Author(s):  
Gaby S. Langendijk ◽  
Diana Rechid ◽  
Daniela Jacob

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2°C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>


2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.


2020 ◽  
Vol 33 (14) ◽  
pp. 5885-5903 ◽  
Author(s):  
Elinor R. Martin ◽  
Cameron R. Homeyer ◽  
Roarke A. McKinzie ◽  
Kevin M. McCarthy ◽  
Tao Xian

AbstractChanges in tropical width can have important consequences in sectors including ecosystems, agriculture, and health. Observations suggest tropical expansion over the past 30 years although studies have not agreed on the magnitude of this change. Climate model projections have also indicated an expansion and show similar uncertainty in its magnitude. This study utilizes an objective, longitudinally varying, tropopause break method to define the extent of the tropics at upper levels. The location of the tropopause break is associated with enhanced stratosphere–troposphere exchange and thus its structure influences the chemical composition of the stratosphere. The method shows regional variations in the width of the upper-level tropics in the past and future. Four modern reanalyses show significant contraction of the tropics over the eastern Pacific between 1981 and 2015, and slight but significant expansion in other regions. The east Pacific narrowing contributes to zonal mean narrowing, contradicting prior work, and is attributed to the use of monthly and zonal mean data in prior studies. Six global climate models perform well in representing the climatological location of the tropical boundary. Future projections show a spread in the width trend (from ~0.5° decade−1 of narrowing to ~0.4° decade−1 of widening), with a narrowing projected across the east Pacific and Northern Hemisphere Americas. This study illustrates that this objective tropopause break method that uses instantaneous data and does not require zonal averaging is appropriate for identifying upper-level tropical width trends and the break location is connected with local and regional changes in precipitation.


2016 ◽  
Vol 12 (8) ◽  
pp. 1645-1662 ◽  
Author(s):  
Emmanuele Russo ◽  
Ulrich Cubasch

Abstract. The improvement in resolution of climate models has always been mentioned as one of the most important factors when investigating past climatic conditions, especially in order to evaluate and compare the results against proxy data. Despite this, only a few studies have tried to directly estimate the possible advantages of highly resolved simulations for the study of past climate change. Motivated by such considerations, in this paper we present a set of high-resolution simulations for different time slices of the mid-to-late Holocene performed over Europe using the state-of-the-art regional climate model COSMO-CLM. After proposing and testing a model configuration suitable for paleoclimate applications, the aforementioned mid-to-late Holocene simulations are compared against a new pollen-based climate reconstruction data set, covering almost all of Europe, with two main objectives: testing the advantages of high-resolution simulations for paleoclimatic applications, and investigating the response of temperature to variations in the seasonal cycle of insolation during the mid-to-late Holocene. With the aim of giving physically plausible interpretations of the mismatches between model and reconstructions, possible uncertainties of the pollen-based reconstructions are taken into consideration. Focusing our analysis on near-surface temperature, we can demonstrate that concrete advantages arise in the use of highly resolved data for the comparison against proxy-reconstructions and the investigation of past climate change. Additionally, our results reinforce previous findings showing that summertime temperatures during the mid-to-late Holocene were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. However, in winter, the model does not correctly reproduce the same amplitude of changes evident in the reconstructions, even if it captures the main pattern of the pollen data set over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though the wintertime discrepancies between the two data sets in some areas are most likely due to high pollen uncertainties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability.


2014 ◽  
Vol 10 (1) ◽  
pp. 79-90 ◽  
Author(s):  
D. J. Hill ◽  
A. M. Haywood ◽  
D. J. Lunt ◽  
S. J. Hunter ◽  
F. J. Bragg ◽  
...  

Abstract. The Pliocene Model Intercomparison Project (PlioMIP) is the first coordinated climate model comparison for a warmer palaeoclimate with atmospheric CO2 significantly higher than pre-industrial concentrations. The simulations of the mid-Pliocene warm period show global warming of between 1.8 and 3.6 °C above pre-industrial surface air temperatures, with significant polar amplification. Here we perform energy balance calculations on all eight of the coupled ocean–atmosphere simulations within PlioMIP Experiment 2 to evaluate the causes of the increased temperatures and differences between the models. In the tropics simulated warming is dominated by greenhouse gas increases, with the cloud component of planetary albedo enhancing the warming in most of the models, but by widely varying amounts. The responses to mid-Pliocene climate forcing in the Northern Hemisphere midlatitudes are substantially different between the climate models, with the only consistent response being a warming due to increased greenhouse gases. In the high latitudes all the energy balance components become important, but the dominant warming influence comes from the clear sky albedo, only partially offset by the increases in the cooling impact of cloud albedo. This demonstrates the importance of specified ice sheet and high latitude vegetation boundary conditions and simulated sea ice and snow albedo feedbacks. The largest components in the overall uncertainty are associated with clouds in the tropics and polar clear sky albedo, particularly in sea ice regions. These simulations show that albedo feedbacks, particularly those of sea ice and ice sheets, provide the most significant enhancements to high latitude warming in the Pliocene.


Author(s):  
Eelco J. Rohling

On the work floor, research on past climates is known as paleoclimatology, and research on past oceans as paleoceanography. But they are very tightly related, and we shall discuss both combined under the one term of paleoclimatology. Within paleoclimatology, interests are spread over three fundamental fields. The first field is concerned with dating ancient evidence and is referred to as chronological studies. These studies are essential because all records of past climate change need to be dated as accurately as possible to ensure that we know when the studied climate changes occurred, how fast they were, and whether changes seen in various components of the climate system happened at the same time or at different times. The second field concerns observational studies, where the observations can be of different types. Some are direct measurements; for example, sunspot counts or temperature records. Some are historical, written accounts of anecdotal evidence, such as reports on the frequency of frozen rivers, floods, or droughts. Such records are very local and often subjective, so they are usually no good as primary evidence. But they can offer great support and validation to reconstructions from other tools. Besides direct and anecdotal data, we encounter the dominant type of evidence used in the discipline. These are the so- called proxy data, or proxies. Proxies are indirect measures that approximate (hence the name proxy) changes in important climate- system variables, such as temperature, CO2 concentrations, nutrient concentrations, and so on. This chapter outlines some of the most important proxies. The third field in paleoclimatology concerns modeling. It employs numerical models for climate system simulation and simpler classes of so-called box- models. Numerical climate models range from Earth System models that are relatively crude and can therefore be set to run simulations of many thousands of years, to very complex and refined coupled models that are computationally very greedy and thus give simulations of great detail but only over short intervals of time. Box- models are much simpler and faster to run, and they are most used in modeling of the carbon cycle or other geochemical properties.


Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1102 ◽  
Author(s):  
Rishabh Gupta ◽  
Rabin Bhattarai ◽  
Ashok Mishra

The use of global and regional climate models has been increasing in the past few decades, in order to analyze the future of natural resources and the socio-economic aspects of climate change. However, these climate model outputs can be quite biased, which makes it challenging to use them directly for analysis purpose. Therefore, a tool named Climate Data Bias Corrector was developed to correct the bias in climatic projections of historical and future periods for three primary climatic variables—rainfall, temperature (maximum and minimum), and solar radiation. It uses the quantile mapping approach, known for its efficiency and low computational cost for bias correction. Its Graphical User Interface (GUI) was made to be feasible to take input and give output in commonly used file formats—comma and tab delimited file formats. It also generates month-wise cumulative density function (CDF) plot of a random station/grid to allow the user to investigate the effectiveness of correction statistically. The tool was verified with a case study on several agro-ecological zones of India and found to be efficient.


2000 ◽  
Vol 2 (3) ◽  
pp. 163-182 ◽  
Author(s):  
Alan F. Hamlet ◽  
Dennis P. Lettenmaier

Ongoing research by the Climate Impacts Group at the University of Washington focuses on the use of recent advances in climate research to improve streamflow forecasts at seasonal-to-interannual, decadal, and longer time scales. Seasonal-to-interannual climate forecasting capabilities have advanced significantly in the past several years, primarily because of improvements in the understanding of, and an ability to forecast, El Niño/Southern Oscillation (ENSO) at seasonal/interannual time scales, and because of better understanding of longer time scale climate phenomena like the Pacific Decadal Oscillation (PDO). These phenomena exert strong controls on climate variability along the Pacific Coast of North America. The streamflow forecasting techniques we have developed for Pacific Northwest (PNW) rivers are based on climate forecasts that facilitate longer lead times (as much as a year) than the methods that are traditionally used for water management (maximum forecast lead times of a few months). At interannual time scales, the simplest of these techniques involves resampling meteorological data from previous years identified to be in similar climate categories as are forecast for the coming year. These data are then used to drive a hydrology model, which produces an ensemble of streamflow forecasts that are analogous to those that result from the well-known Extended Streamflow Prediction (ESP) method. This technique is a relatively simple, but effective, way of incorporating long-lead climate information into streamflow forecasts. It faithfully captures the history of observed climate variability. Its main limitation is that the sample size of observed events for some climate categories is small because of the length of the historic record. Furthermore, it is unable to capture important aspects of global change, which may interact with shorter term variations through changes in climate phenomena like ENSO and PDO. An alternative to the resampling method is to use nested regional climate models to produce the long-lead climate forecasts. Success using this approach has been hindered to some degree by the bias that is inherent in climate models, even when downscaled using regional nested modeling approaches. Adjustment or correction for this bias is central to the use of climate model output for hydrologic forecasting purposes. Approaches for dealing with climate model bias in the context of global and meso-scale are presently an area of active research. We illustrate an experimental application of the nested climate modeling approach for the Columbia River Basin, and compare it with the simpler resampling method. At much longer time scales, changes in Columbia River flows that might be associated with global climate change are of considerable concern in the PNW, given recent Endangered Species Act listing of certain salmonid species, and the increase in water demand that is expected to follow increases in human population in the region. Many of the same general challenges associated with the spatial downscaling of climate forecasts are present in these long-range investigations. Additional uncertainties exist in the ability of climate models to predict the effects of changing greenhouse gas concentrations. These uncertainties tend to dominate the results, and lead us to use relatively simplemethods of downscaling seasonal temperature and precipitation to interpret the implications of alternative climate scenarios on PNW water resources.


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