Uncertainty In Trends Research Articles

Ground-based climate data show evidence of warming and intensification of the seasonal rainfall cycle during the 1960–2020 period in Yangambi, central Congo Basin

Meteorological stations are rare in central Africa, which leads to uncertainty in regional climatic trends. This is particularly problematic for the Congo Basin, where station coverage decreased significantly during the last few decades. Here, we present a digitized dataset of daily temperature and precipitation from the Yangambi biosphere reserve, covering the period 1960–2020 (61 years) and located in the heart of the Congo Basin. Our results confirm a long-term increase in temperature and temperature extremes since the 1960s, with strong upward trends since the early 1990s. Our results also indicate a drying trend for the dry season and intensification of the wet season since the early 2000s. Ongoing warming and increasing precipitation seasonality and intensity already have a significant impact on crop yields in Yangambi. This calls for urgent development of climate-smart and dynamic agriculture and agroforestry systems. We conclude that systematic digitization and climate recording in the Congo Basin will be critical to improve much-needed gridded benchmark datasets of climatic variables.

Uncertainty of energy consumption and CO2 emissions in the building sector in China

Uncertainty is a major concern in projecting the energy consumption and CO2 emissions from buildings with implications for the formulation of mitigation and adaptation measures. Due to multiple uncertainties, it is difficult to assess how the energy consumption of buildings in China and the associated CO2 emissions will develop in the coming decades. Aiming to give a more accurate picture of the uncertainty of the driving factors, we develop a bottom-up national energy technology model for building sector (NET-Building) to depict the evolutionary trajectories of energy consumption and CO2 emissions in China's building sector by considering the uncertainty of future evolution trends of key variables that affecting the energy consumption and CO2 emissions. The results show that China's building energy consumption and CO2 emissions will peak in 2025–2040 and 2025–2035, with peak energy consumption and CO2 emissions varying from 742–1585 Mtce and 2238–4211 Mt, respectively. Meanwhile, the electricity consumption in the building sector in China will reach 2515–7242 TWh by 2050, indicating a large variation range caused by uncertainties.

Large discrepancies among remote sensing indices for characterizing vegetation growth dynamics in Nepal

Mountain ecosystems provide multiple ecosystem services and are “natural laboratories” to understand ecosystem responses to global change. Because of the inaccessibility and the high cost of field surveys, remote sensing indices are the major and sometimes the only measures to monitor the vegetation growth dynamics in mountains. However, there are large discrepancies in those indices that should be quantified in mountainous regions. This case study in Nepal, a highly mountainous region, explores the consistency and inconsistency of six widely used remote sensing indices in monitoring vegetation growth from 2000 to 2020. The study considers three greenness indices of normalized difference vegetation indices (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), one cover index of leaf area index (LAI), and two productivity indices of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF). We find high spatial consistency in the multiyear means (r = 0.79∼1, N = 4300, p < 0.01), especially in the highlands and between EVI and NIRv, and a logarithmic relationship between greenness indices or GOSIF and LAI or GPP. In contrast, the long-term trends differ substantially by index and space. Only 7% of the lands show synchronized significant increase though all the indices show a widespread increasing tendency (77∼87% of the lands). The prevalent non-significant changes of all the indices primarily contribute to the trend uncertainties, especially in the highlands. The inconsistencies between greenness and productivity indices and in them further exaggerate the uncertainties. Our results emphasize the large discrepancies of remote sensing indices in quantifying mountain vegetation growth dynamics. Larger inconsistency is expected if we consider disparities among the quality-control schemes, study seasons, remote sensing models, satellite platforms, and sensors. Reinforced remote sensing data, model improvements and/or new indices are needed for an accurate quantification of the vegetation growth dynamics in mountain regions.

МОЖЛИВОСТІ ГАРМОНІЗАЦІЇ ПЛАТІЖНОГО ПРОСТОРУ УКРАЇНИ: ІНСТИТУЦІЙНИЙ АСПЕКТ

Досліджено особливості й суперечності cashless-економіки в інституційній інтерпретації, проаналізовано світовий досвід її розбудови в контексті гармонізації процесів створення cashless-економіки в Україні. Для досягнення поставлених цілей охарактеризовано сучасний стан розвитку цифрового фінансового сектору України, показано особливості циркуляції ліквідності за воєнного стану і на цій основі зроблено висновок, що, незважаючи на формування в Україні надсучасного платіжного простору, в кризових умовах можливим є повернення до архаїчних механізмів розрахунків, здатних підтримати цілісність економічної системи. Інституційний аналіз дозволив зробити висновки, що цифрові фінансові послуги є суперечливим явищем, яке, з одного боку, може частково компенсувати брак ринкових інститутів і цінностей (особливо в окремих країнах залежного розвитку), а з іншого – не дозволяє формуватися повноцінному суспільному попиту на ринкові інститути. На основі аналізу світового досвіду впровадження принципів безготівкової економіки показано, що, хоча обсяги безготівкових розрахунків зростають, вони не переважають серед пріоритетів європейських платіжних стратегій і не домінують у виборі суб’єктів ринку як у розвинутих країнах, так і в країнах, що розвиваються. Досвід формування cashless-економіки в країнах, що розвиваються, доводить, що навіть за авангардного розвитку мобільних фінансових послуг можуть виникнути умови для повернення до активного використання готівкових розрахунків. Зважаючи на те, що в ситуації зростаючої невизначеності трендів політичного, економічного, соціального розвитку безготівкові розрахунки не можуть забезпечити фінансової усталеності, сьогодні важливо використовувати модель портфельного підходу до ліквідних активів, яка передбачає раціональну пропорцію між картковими і готівковими розрахунками суб’єктів.

Assigning trend-based conservation status despite high uncertainty.

Estimates of temporal trends in species' occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision-making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.

Moisture sources for precipitation variability over the Arabian Peninsula

We apply the Lagrangian-based moisture back trajectory method to two reanalysis datasets to determine the moisture sources for wet season precipitation over the Arabian Peninsula, defined as land on the Asian continent to the south of the Turkish border and west of Iran. To accomplish this, we make use of the evaporative source region between 65°W–120°E and 30°S–60°N, which is divided into twelve sub-regions. Our comparison of reanalyses and multiple observations allows us to validate datasets and highlight broad-scale similarities in characteristics, notwithstanding some inconsistencies in the southwest AP. The results indicate north-to-south spatiotemporal heterogeneity in the characteristics of dominant moisture sources. In the north, moisture for precipitation is mainly sourced from midlatitude land and water bodies, such as the Mediterranean and Caspian Seas. Areas further south are dependent on moisture transport from the Western Indian Ocean and parts of the African continent. The El Niño-Southern Oscillation (ENSO) exhibits an overall positive but sub-seasonally varying influence on the precipitation variability over the region, with noticeable moisture anomalies from all major source regions. A significant drying trend exists over parts of the Peninsula, which both reanalyses partially attribute to anomalies in the moisture advection from the Congo Basin and South Atlantic Ocean. However, considerable uncertainty in evaporation trends over the terrestrial evaporative sources in observations warrants additional modeling studies to further our understanding of key processes contributing to the negative trends.

Quantifying Multi-Source Uncertainties in GRACE-Based Estimates of Groundwater Storage Changes in Mainland China

The Gravity Recovery and Climate Experiment (GRACE) satellites have been widely used to estimate groundwater storage (GWS) changes, yet their uncertainties related to the multi-source datasets used are rarely investigated. This study focuses on quantifying the uncertainties of GRACE GWS estimates in mainland China during 2003–2015, by generating a total of 3456 solutions from the combinations of multiple GRACE products and auxiliary datasets. The Bayesian model averaging (BMA) approach is used to derive the optimal estimates of GWS changes under an uncertainty framework. Ten river basins are further identified to analyze the estimated annual GWS trends and uncertainty magnitudes. On average, our results show that the BMA-estimated annual GWS trend in mainland China is −1.93 mm/yr, whereas its uncertainty reaches 4.50 mm/yr. Albeit the estimated annual GWS trends and uncertainties vary across river basins, we found that the high uncertainties of annual GWS trends are tied to the large differences between multiple GRACE data and soil moisture products used in the GWS solutions. These findings highlight the importance of paying more attention to the existence of multi-source uncertainties when using GRACE data to estimate GWS changes.

Bayesian Noise Modelling for State Estimation of the Spread of COVID-19 in Saudi Arabia with Extended Kalman Filters.

The epistemic uncertainty in coronavirus disease (COVID-19) model-based predictions using complex noisy data greatly affects the accuracy of pandemic trend and state estimations. Quantifying the uncertainty of COVID-19 trends caused by different unobserved hidden variables is needed to evaluate the accuracy of the predictions for complex compartmental epidemiological models. A new approach for estimating the measurement noise covariance from real COVID-19 pandemic data has been presented based on the marginal likelihood (Bayesian evidence) for Bayesian model selection of the stochastic part of the Extended Kalman filter (EKF), with a sixth-order nonlinear epidemic model, known as the SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Dead) compartmental model. This study presents a method for testing the noise covariance in cases of dependence or independence between the infected and death errors, to better understand their impact on the predictive accuracy and reliability of EKF statistical models. The proposed approach is able to reduce the error in the quantity of interest compared to the arbitrarily chosen values in the EKF estimation.

Modulation Effect of the Annual Cycle on Interdecadal Warming Trends over the Tibetan Plateau during 1998–2020

Abstract The Tibetan Plateau is a sensitive area of global climate change, where few conventional observations exist. Satellite AMSU-A microwave temperature sounding observations of brightness temperature (TB) are located in the absorption band of oxygen, which is well mixed in the atmosphere, and have microwave frequencies varying from 50.3 to 57.6 GHz. Therefore, AMSU-A TB observations at different sounding channels reflect atmospheric temperatures at different altitudes. In this study, AMSU-A TB observations during 1998–2020 from five polar-orbiting environmental meteorological satellites (POESs) are employed to investigate the interdecadal warming/cooling trends over the Tibetan Plateau. A limb correction is first applied to all AMSU-A channels before using TB observations at all fields of view for examining geographic distributions and differences of global warming/cooling trends. It is found that interdecadal trends of upper-tropospheric warming and stratospheric cooling are stronger over the Qinghai Tibetan Plateau than its eastern plain areas. An interdecadal variation of the annual cycle over the Tibetan Plateau is an important factor for the enhanced tropospheric warming trend. We also applied a different approach of significance testing that is based on counting signs of local trends (sign test) and confirmed that the detected significant local trends were not a result of chance. In addition, high-frequency noise in TB observations with periods smaller than annual and semiannual oscillations do not affect the climate trends of TB very much, but significantly reduced the uncertainty of the TB trends over the Tibetan Plateau.

Random year intercepts in mixed models help to assess uncertainties in insect population trends

Abstract An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter‐annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p‐values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.

Global Mean Sea Level Rise Inferred From Ocean Salinity and Temperature Changes

AbstractBarystatic sea level rise (SLR) caused by the addition of freshwater to the ocean from melting ice can in principle be recorded by a reduction in seawater salinity, but detection of this signal has been hindered by sparse data coverage and the small trends compared to natural variability. Here, we develop an autoregressive machine learning method to estimate salinity changes in the global ocean from 2001 to 2019 that reduces uncertainties in ocean freshening trends by a factor of four compared to previous estimates. We find that the ocean mass rose by 13,000 ± 3,000 Gt from 2001 to 2019, implying a barystatic SLR of 2.0 ± 0.5 mm/yr. Combined with SLR of 1.3 ± 0.1 mm/yr due to ocean thermal expansion, these results suggest that global mean sea level rose by 3.4 ± 0.6 mm/yr from 2001 to 2019. These results provide an important validation of remote‐sensing measurements of ocean mass changes, global SLR, and global ice budgets.

Strength, weakness, opportunities, and threats (SWOT) analysis of fuel cells in electric vehicles

Fuel cell electric vehicles have the advantages of simple assembly, higher efficiency, and quiet operation compared to any other propulsion method. While there is a clear road map on the future of fuel cell electric vehicles, there is still a need for a paradigm shift to relent on using fossil fuels as soon as possible. The majority of the reviewed literature focuses on enhancing the current performance of the fuel cell electric vehicle technology for the transportation sector. However, detailed information for policy makers and the research communities on the current state of the technology is evidently missing. This study presents a detailed and holistic examination of fuel cell electric vehicle technology's current state and prospects. A systematic SWOT analysis on the technology is presented to highlight major factors impeding the proliferation of the technology up to widespread commercialization. Despite the progress made, the uncertainty of market trends requires further studies to allow fuel cells to compete fairly with existing infrastructure like the one available for conventional internal combustion engines.

Carbon assessment of building shell options for eco self-build community housing through the integration of building energy modelling and life cycle analysis tools

To achieve the UK's net-zero carbon transition by 2050, new homes need to be designed to high energy efficiency standards and minimise life cycle carbon emissions. This study develops a method that integrates building energy modelling (BEM) and life cycle assessment (LCA) tools, IES Virtual Environment (IES VE) and One Click LCA, to conduct a life cycle assessment of the operational and embodied carbon impacts of a typical terraced building shell in an eco self-build community housing (ESBC) project, using Water Lilies as a case study. It evaluates and compares the 60-year life cycle carbon impacts of six design options that apply different insulation materials to the case study building shell. The results are based on grid-connected Future Energy Scenarios (FES) that have different carbon intensities predicted over time and the microgrid-connected Water Lilies Community Energy (WLCE) scenario that is operationally net-zero carbon. Depending on the FES, embodied carbon accounted between 59 and 80% of total emissions compared to 20–41% from operational carbon across design options. The results show that operational carbon is highly sensitive to the percentage of renewables in the energy mix of the grid, which is difficult to predict, and can significantly influence the design choice. As such, future researchers should account for predicted changes to the carbon intensity of the grid by applying a similar method to that proposed. Overall, the integration of IES VE and One Click LCA provided a more streamlined life cycle assessment to help inform early design decisions by enabling energy consumption simulation in BEM to assess operational carbon emissions and the automated transfer of building materials from the BEM model to the LCA tool to assess embodied carbon emissions. The limitations included the unavailability of material-specific data in early design stage plans, the inability to automatically transfer the timber frame structure from IES VE to One Click LCA, and the uncertainty of future energy trends.

Atmospheric verification of emissions reductions on paths to deep decarbonization

A central challenge for sustaining international cooperation to cut global greenhouse gas emissions is confidence that national policy efforts are leading to a meaningful impact on the climate. Here, we apply a detection protocol to determine when the measurable signal of atmospheric CO2 can be distinguished from the noise of the carbon cycle and uncertainties in emission trends. We test that protocol with a database of 226 emission mitigation scenarios—the universe of scenarios vetted by the Intergovernmental Panel on Climate Change. These scenarios are descriptive of ‘baseline’ trajectories of emissions trends in the absence of new policies along with trajectories that reflect substantial policy efforts to stop warming at 1.5 °C–2 °C above pre-industrial levels, as embodied in the Paris Agreement. The most aggressive mitigation scenarios (i.e. 1.5 °C) require 11–16 years to detect a signal of demonstrable progress from the noise; 2 °C scenarios lengthen detection by at least a decade. As more climate policy analysts face the reality that goals of 1.5 °C–2 °C seem infeasible, they have developed ‘overshoot’ scenarios with emissions that rise above the agreed goal and then, later on, fall aggressively to achieve it. These pathways come at the political cost of a 1–2 decade delay in detection, even for the 1.5 °C scenarios. The Paris Agreement requires a global ‘stocktake’ that interrogates national mitigation efforts; our results suggest that this effort must grapple with the question of when the world can gain confidence that the diplomacy on climate is demonstrably making an impact.

THE REGIME AND EFFECTIVENESS OF 2022 ECONOMIC SANCTIONS AGAINST THE RUSSIAN FEDERATION

In the early 2022, the world economy is recovering from the decline caused by Covid-19 pandemic –the central banks being prepared to gradually withdraw fiscal and monetary support. The Russian military’s invasion of Ukraine and subsequent sanctions have reversed this trend and risks and uncertainty have suddenly returned amid rising energy, food and other important commodity prices, inflationary pressures, deterioration in financial and labor market conditions. This paper outlines the general framework of economic sanctions, reviews the sanctions adopted by the international community against Russia in the first 100 days of brutal, unjustified and unprovoked illegal military aggression against Ukraine, and identifies their effectiveness and impact on Russia’s economic and financial system.

NEURAL NETWORK MODEL FOR AVERAGE ANNUAL NET EARNINGS DETERMINATION IN EU ECONOMIES

In macroeconomic modeling, the application of artificial neural networks (ANN) for forecasting key macroeconomic variables is present to a large extent. Precise forecasts of economic conditions are of crucial importance for the rational planning of overall economic efficiency in order to reduce the uncertainty of future economic trends. The purpose of constructing economic models is to determine the influence of changes in the value of certain economic variables on the model defined output variable. &#x0D; Starting from earnings as a key concept in economics, the aim of the research is to produce a forecast model for determining the average annual net earnings, depending on the movement of key economic variables, i.e. from measures of economic performance. Based on a series of data relating to the period from 2005 to 2016, for the countries of the European Union, a high-reliability forecast model was developed using the most widely utilized type of ANN – a Multilayer Perceptron with a backpropagation algorithm. Specifically, the forecast model built to determine the earnings showed an average deviation of the real from the forecast size of up to 13%. Understanding and using such and similar models is crucial for policymakers when creating economic policies.

Sensitivity of aerosol optical depth trends using long-term measurements of different sun photometers

Abstract. This work aims to assess differences in the aerosol optical depth (AOD) trend estimations when using high-quality AOD measurements from two different instruments with different technical characteristics and operational (e.g. measurement frequency), calibration and processing protocols. The different types of sun photometers are the CIMEL that is part of AERONET (AErosol RObotic NETwork) and a precision filter radiometer (PFR) that is part of the Global Atmosphere Watch Precision Filter Radiometer network. The analysis operated for two wavelengths (500 and 501 and 870 and 862 nm for CIMEL–PFR) in Davos, Switzerland, for the period 2007–2019. For the synchronous AOD measurements, more than 95 % of the CIMEL–PFR AOD differences are within the WMO-accepted limits, showing very good measurement agreement and homogeneity in calibration and post-correction procedures. AOD trends per decade in AOD for Davos for the 13-year period of analysis were approximately −0.017 and −0.007 per decade for 501 and 862 nm (PFR), while the CIMEL–PFR trend differences have been found 0.0005 and 0.0003, respectively. The linear trend difference for 870 and 862 nm is larger than the linear fit standard error. When calculating monthly AODs using all PFR data (higher instrument frequency) and comparing them with the PFR measurements that are synchronous with CIMEL, the trend differences are smaller than the standard error. Linear trend differences of the CIMEL and PFR time series presented here are not within the calculated trend uncertainties (based on measurement uncertainty) for 870 and 862 nm. On the contrary, PFR trends, when comparing high- and low-measurement-frequency datasets are within such an uncertainty estimation for both wavelengths. Finally, for time-varying trends all trend differences are well within the calculated trend uncertainties.

Comparative analysis of ARIMA and double exponential smoothing for forecasting rice sales in fair price shop

One of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.

Updated trends of the stratospheric ozone vertical distribution in the 60° S–60° N latitude range based on the LOTUS regression model

Abstract. This study presents an updated evaluation of stratospheric ozone profile trends in the 60∘ S–60∘ N latitude range over the 2000–2020 period using an updated version of the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) regression model that was used to evaluate such trends up to 2016 for the last WMO Ozone Assessment (2018). In addition to the derivation of detailed trends as a function of latitude and vertical coordinates, the regressions are performed with the datasets averaged over broad latitude bands, i.e. 60–35∘ S, 20∘ S–20∘ N and 35–60∘ N. The same methodology as in the last assessment is applied to combine trends in these broad latitude bands in order to compare the results with the previous studies. Longitudinally resolved merged satellite records are also considered in order to provide a better comparison with trends retrieved from ground-based records, e.g. lidar, ozonesondes, Umkehr, microwave and Fourier transform infrared (FTIR) spectrometers at selected stations where long-term time series are available. The study includes a comparison with trends derived from the REF-C2 simulations of the Chemistry Climate Model Initiative (CCMI-1). This work confirms past results showing an ozone increase in the upper stratosphere, which is now significant in the three broad latitude bands. The increase is largest in the Northern and Southern Hemisphere midlatitudes, with ∼2.2 ± 0.7 % per decade at ∼2.1 hPa and ∼2.1 ± 0.6 % per decade at ∼3.2 hPa respectively compared to ∼1.6 ± 0.6 % per decade at ∼2.6 hPa in the tropics. New trend signals have emerged from the records, such as a significant decrease in ozone in the tropics around 35 hPa and a non-significant increase in ozone in the southern midlatitudes at about 20 hPa. Non-significant negative ozone trends are derived in the lowermost stratosphere, with the most pronounced trends in the tropics. While a very good agreement is obtained between trends from merged satellite records and the CCMI-1 REF-C2 simulation in the upper stratosphere, observed negative trends in the lower stratosphere are not reproduced by models at southern and, in particular, at northern midlatitudes, where models report an ozone increase. However, the lower-stratospheric trend uncertainties are quite large, for both measured and modelled trends. Finally, 2000–2020 stratospheric ozone trends derived from the ground-based and longitudinally resolved satellite records are in reasonable agreement over the European Alpine and tropical regions, while at the Lauder station in the Southern Hemisphere midlatitudes they show some differences.

Sign of Observed California Temperature Trends Depends on Data Set Homogenization: Implications for Weighting and Downscaling

AbstractBecause downscaling methods can yield substantially different projections of future climate, it is imperative to constrain these projections with information from existing observations, while also recognizing observational uncertainty. California is a natural test case to develop observational constraints on future projections given prior studies that have purportedly found contrasting spatial patterns in late‐20th‐century trends of summertime daily‐maximum temperature: cooling along the coast and warming inland. Revisiting this claim, we find that coastal cooling is largely confined to non‐homogenized temperature records while homogenized observations show either non‐significant cooling or warming trends throughout the state. This finding has implications for weighting historical and future climate simulations downscaled using localized constructed analogs. Failure to consider out‐of‐sample skill results in weighted and unweighted Representative Concentration Pathway 8.5 temperature trend estimates differing by 2 K/century or more in California. However, weighted mean estimates that properly account for trend uncertainty do not differ significantly from the unweighted mean.