Patterns Of Temperature Trends Research Articles

Decadal changes in surface CO2 concentrations and CO2 fluxes in a mountain lake

AbstractClimate warming impacts biogeochemical cycles in lakes. However, the factors controlling CO2 dynamics in mountain lakes over multidecadal scales are poorly understood. Here, we capitalized on long‐term monthly data (1995–2022) of oligotrophic mountain Lake Tovel and calculated surface CO2 concentrations and flux by applying geochemical relationships and the thin boundary layer approach. Advanced time‐series and regression modeling was used to determine temporal patterns and environmental parameters explaining surface CO2 concentrations and flux. Surface CO2 concentrations were highest from 2009 to 2017 (annual mean: 109.1 μmol CO2 L−1) but lower before and after this period. Concomitantly, the air–water CO2 flux (μmol CO2 m−2 d−1) showed a period of lowest (mean1995–2010: 6.4 ± 0.7), highest (mean2011–2017: 35.7 ± 2.1), and intermediate emissions (mean2018–2022: 19.3 ± 4.7). Temporal modeling showed that hypolimnetic and deep hypolimnetic dissolved oxygen (DO) had the same change points and trends as surface CO2 concentrations. In multiple linear regression, hypolimnetic DO, silica, and the standardized precipitation index (pseudo‐R2adj. = 0.62; p < 0.01) best predicted annual mean surface CO2 concentrations. Regression results and the overlap between temporal trend patterns indicated that surface CO2 concentrations of Lake Tovel were positively influenced by external (loading of allochthonous carbon) and internal (lake autumn mixing) factors. The recent decline in surface CO2 concentrations from the year 2018 was attributed to increased stratification that offset lake autumn mixing and thus lead to the observed decline. These results help us to better understand the carbon cycle in mountain lakes in a changing climate.

Assessment of observed temperature trend patterns of Bhubaneswar city, India with special prominence on future projections using SimCLIM climate model and farmer’s perception

<p>Temperature dynamics is a widely recognized indicator of the global warming phenomenon. Changes in temperature patterns in Bhubaneswar, India, were assessed by examining the monthly minimum and maximum temperature data for 60 years (1956–2015) sourced from the Indian Meteorological Department. SimCLIM climate change risk assessment software was used for projecting the temperature regime for four different representative concentration pathways. Further, a survey of 112 farmers was conducted to understand their perceptions of temperature variations in and around Bhubaneswar city using a multi-stage sampling technique. Mann–Kendall statistics and linear regression were used to analyze the monthly temperature data and trend detection. The study reveals a change of +4%,-4.44%, and +1.09% in the mean monthly maximum, minimum, and annual temperature. The results of the future projection show a temperature change of 0.81°C for RCP 2.6, 1.12°C for RCP 4.5, 1.03°C for RCP 6.0, and 1.54°C for RCP 8.5 for the year 2050. Confirming the analysis findings, most of the interviewed farmers also perceived increasing temperatures and decreasing precipitation in and around the city. The study outcome of temperature trend analysis and future projections will be helpful for farmers and policymakers in formulating adaptation strategies to climate change.</p>

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Assessment of temporal instability in the applied ecology and conservation evidence base.

Outcomes of meta-analyses are increasingly used to inform evidence-based decision making in various research fields. However, a number of recent studies have reported rapid temporal changes in magnitude and significance of the reported effects which could make policy-relevant recommendations from meta-analyses to quickly go out of date. We assessed the extent and patterns of temporal trends in magnitude and statistical significance of the cumulative effects in meta-analyses in applied ecology and conservation published between 2004 and 2018. Of the 121 meta-analyses analysed, 93% showed a temporal trend in cumulative effect magnitude or significance with 27% of the datasets exhibiting temporal trends in both. The most common trend was the early study effect when at least one of the first 5 years effect size estimates exhibited more than 50% magnitude difference to the subsequent estimate. The observed temporal trends persisted in majority of datasets once moderators were accounted for. Only 5 datasets showed significant changes in sample size over time which could potentially explain the observed temporal change in the cumulative effects. Year of publication of meta-analysis had no significant effect on presence of temporal trends in cumulative effects. Our results show that temporal changes in magnitude and statistical significance in applied ecology are widespread and represent a serious potential threat to use of meta-analyses for decision-making in conservation and environmental management. We recommend use of cumulative meta-analyses and call for more studies exploring the causes of the temporal effects.

ТЕКУЩИЕ ТРЕНДЫ ПРИПОВЕРХНОСТНОЙ ТЕМПЕРАТУРЫ ВОЗДУХА В АНТАРКТИДЕ ПО ДАННЫМ РЕАНАЛИЗА И НАЗЕМНЫХ СТАНЦИЙ

This study utilizes monthly-mean records of near-surface air temperature from ground-based stations in Antarctica spanning the time period of 1980 to 2022 to estimate temperature trends in the region. Analysis of in situ data confirms the prevailing warming tendencies over the continent, with positive trends observed at 11 out of the 18 examined stations. Annual mean temperature trends reach 0.40 °C/decade, with the most significant warming observed in the Antarctic Peninsula, West Antarctica, and Inner Antarctica. Relatively weaker and mixed temperature trends occurred in the coastal areas of East Antarctica. Throughout the year, the spring season exhibited the most consistent upward trends across the continent. Comparing current temperature trends with earlier estimates has revealed increased warming at most Antarctic stations in recent years. Four widely used reanalysis datasets – ERA5, JRA55, MERRA2, and CFSR – were examined to evaluate their ability to reproduce the observed temperature trends in situ. Among these four datasets temperature trends inferred from ERA5 provided the best fit to the station data. However, even in ERA5, the uncertainty in trend estimates was comparable to the magnitude of trend variation between stations. This suggests a limited ability of available reanalysis datasets to accurately reproduce the spatial distribution and patterns of temperature trends across the continent.

Temporally extended satellite-derived surface air temperatures reveal a complete warming picture on the Tibetan Plateau

The Tibetan Plateau (TP) is the highest plateau in the world, which imposes the intense thermal and dynamical forcings on the atmosphere and then impacts the climate in its surroundings. The TP has been undergoing a rapid warming, which accelerates glacial melting, and causes more natural hazards. Although the warming on the TP has been widely investigated, there is no complete picture of its thermal status during the past decades for lack of high-quality, long-term, spatiotemporal-continuous observations. The number of weather stations are rather limited and are mainly located in the east of the TP. The analysis based on these stations is confronted with the spatial representativeness problem. On the other hand, Satellites can monitor the earth seamlessly in space and time, but reliable land surface temperatures (LSTs) have only been available in recent 20 years, and moreover their physical meaning also differs from that of the most commonly used surface air temperatures (SATs). For climate change research, the period length of these satellite LSTs is too short to obtain a definitive conclusion. In this study, the entire algorithm consists of two primary steps. One is to develop an stacking-based ensemble learning algorithm to convert LSTs to SATs with the random forest model as both base learner and meta learner. The other is to construct a Bayesian-based temporal extension algorithm to merge satellite SATs and station SATs to obtain long-term, spatiotemporal-continuous SATs. After validating the reliability of these SATs and the warming trends based on them, 60 years (1961–2020) of SATs on the TP are implemented to examine the warming status of the TP. The spatial pattern of temperature trends illustrates that the warming occurs almost everywhere on the TP, and lots of areas with intensive warming, cannot be detected only based on station observations, especially in the western part of the TP. Similarly, ERA5-Land and CRU datasets underestimate the warming in these areas. The newly-derived warming rate arrives at 0.03 ° C/year and is 50% greater than those computed based on ERA5-Land and CRU dataset, implying an unexpected severe threat to the cryosphere.

Identifying temporal trend patterns of temperature means and extremes over the Central Highlands, Vietnam

Identifying temporal trend patterns of temperature means and extremes over the Central Highlands, Vietnam

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.

Modelling Current Temperature Trends

Current trends in Northern Hemisphere and Central England temperatures are estimated using a variety of statistical signal extraction and filtering techniques and their extrapolations are compared with the pre dictions from coupled atmospheric-ocean general circulation models. Ear lier warming trend epochs are also analysed and compared with the current warming trend, suggesting that the long-run patterns of temperature trends should also be considered alongside the current emphasis on global warming.

Trends of Temperature Extreme Indices over Arusha and Kilimanjaro Regions in Tanzania

The study aimed at analyzing the trends and variability of temperature extremes over the northeastern highlands in Tanzania, specifically over Arusha and Kilimanjaro regions. Quality controlled mean monthly, daily maximum and minimum temperature data for the period 1961 to 2020, obtained from Tanzania Meteorological Authority, were used in the study. Rclimdex and the National Climate Monitoring Products (NMCP) software, developed by the World Meteorological Organization (WMO), were used for computation of the indices at a monthly, seasonal and annual time scale. The computed indices were also subjected to trend analysis to determine their direction and magnitude of change. Extraction and assessment of the top five highest and lowest maximum and minimum temperatures were also done. Increasing trends of temperature anomalies for seasonal and annual timescales were observed for both Arusha and Kilimanjaro regions. Also, the increasing trends of warm and extreme warm days and nights and relatively increasing trends of cold and extreme cold days and nights were observed for both regions. The highest ever recorded temperatures since the establishment of the two stations were 36.3?C observed on 16th February 2011 and 38.6?C observed on 22nd February 2005 for Arusha and Kilimanjaro respectively. These results indicate that The last two decades have been characterized by enhanced warming, which is consistent with overall global temperature trend patterns as depicted in recent IPCC reports and the report of the State of Climate in Africa.

Spatio-temporal trend analysis of rainfall using parametric and non-parametric tests: case study in Uttarakhand, India

This study investigates the spatial and temporal patterns of trends and magnitude of rainfall on monthly, seasonal and annual time scales of 13 districts of Uttarakhand State located in Central Himalayan region of India. The temporal trend was analyzed using Mann-Kendall (MK), Modified Mann-Kendall (MMK), and Kendall Rank Correlation (KRC) tests at 10%, 5%, and 1% significance levels. The magnitude (slope) of rainfall trend (mm/year) was determined using Theil-Sen’s Slope (TSS) and Simple Linear Regression (SLR) tests. The autocorrelation coefficient (ACC) of three different time series was calculated at one-time lag and were tested at 10%, 5%, and 1% levels of significance for the application of MMK test. The results of analysis revealed significant positive and negative trends were observed in monthly, seasonal, and annual rainfall time series in all 13 districts of Uttarakhand state. The spatial variation of the trends based on monthly, seasonal, and annual rainfall time series data was interpolated using the Thiessen polygon (TP) method in ArcGIS 10.2 environment. The maps of spatial variability of rainfall trends were developed to help local stakeholders and water resource managers to understand the risk and vulnerability related to climate change in the region.

Spatial-temporal trend analysis of seasonal and annual rainfall (1966–2015) using innovative trend analysis method with significance test

This study investigates the spatial and temporal patterns of trends on seasonal (pre-monsoon, monsoon, post-monsoon, and winter) and annual rainfall time series data (1966–2015) at 13 stations located in the central Himalayan region of the Uttarakhand State of India. The temporal trend was analyzed using recently proposed innovative trend analysis (ITA) method with significance test. The results of the ITA method were compared with the Mann-Kendall (MK) test at 5% significance level. The spatial variation of the trends in seasonal and annual rainfall series was interpolated using the Thiessen polygon (TP) method in ArcGIS 10.2 environment. The results of comparison revealed that the trend detected by MK test (significantly positive in 3-time series and significantly negative in 6-time series) can be effectively identified using ITA (significantly positive in 19-time series and significantly negative in 32-time series). The ITA method could detect some trends that cannot be observed by the MK test. According to the spatial distribution of MK test, significantly increasing (decreasing) trends were observed in 1 (0), 1 (2), 0 (1), 1 (1), and 1 (1) polygons in pre-monsoon, monsoon, post-monsoon, winter, and annual rainfall data, while the ITA method detected significant trends in 3 (7), 6 (5), 1 (9), 4 (5), and 5 (6) polygons in the study region. The developed maps of spatial variability of rainfall trends may help the stakeholders and/or water resource managers to figure out the risk and vulnerability related to climate change in the study region.

Spatiotemporal analysis of nonlinear trends in precipitation over Germany during 1951–2013 from multiple observation‐based gridded products

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high‐resolution observation‐based gridded precipitation products, the Climatic Research Unit (CRU) time‐series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU‐FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter‐annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time‐varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system.

Hot days and tropical nights in Nigeria: trends and associated large-scale features

The impact on society of extreme temperature events is enormous. This study examines the temporal evolution and trends in mean temperatures (minimum temperature, TN; and maximum temperature, TX) and warm extremes in Nigeria as well as in three regions in Nigeria (Guinea, Savanna, and Sahel) using homogenized daily TN and TX for the period 1971–2012. Warm extremes are defined as days with TX > 35 °C (HotD) and nights with TN > 20 °C (TropN). The modified Mann–Kendall test is used to calculate and assess the statistical significance of trends in the indices. The results at annual and seasonal (JFM, AMJ, JAS, and OND) timescales indicate a significant positive increase in temperatures in Nigeria. The warming in annual and seasonal TN is greater than in TX. The temporal evolution in warm extremes is similar to those of the mean temperatures, with trends in TropN greater than those of the HotD. In all the regions, the temporal patterns of trends in mean temperatures and warm extremes are similar. The indices are characterized by positive trends, with the exception of HotD in Guinea with no data during JAS. Analysis of large-scale atmospheric fields during warm extreme days when both TX and TN exceed their respective 90th percentile thresholds shows that warm extreme days are associated with mid-tropospheric subsidence motion in vertical velocity anomaly that is connected with the core of the thermal low and the net convergent flow. These features are accompanied by positive surface shortwave radiation anomaly coupled with cloud cover reduction.

Upper-ocean temperature trends in the Eastern China Seas during 1976–1996

Temperature in the Eastern China Seas (ECS), including the Bohai, Yellow, and East China seas, is crucially important with regard to weather forecasting and fishery activities of adjacent countries. Although sea surface temperature (SST) in the ECS has demonstrated a dramatically accelerated trend of warming after a regime shift (1976–1996), trends beneath the surface remain poorly understood because of the sparsity of observations. This study used in situ hydrographic data from 1976 to 1996 to examine upperocean temperature trends in the ECS. It was found that the multilevel trends show consistency with that of the surface water; i.e., warming is faster in winter than summer. However, the magnitudes of the trends weaken with increasing depth. Furthermore, the seasonal difference in the upper ocean is mainly associated with the warm currents in the ECS, which implies an essential contribution from horizontal advection. These phenomena could also be detected from data acquired from the routinely observed PN and 34°N sections. The spatiotemporal patterns of temperature trends in the upper ECS extend our understanding beyond the SST, especially highlighting the role of ocean dynamics in forming temperature patterns beneath the surface in comparison with atmospheric effects.

The spatial patterns in long-term temporal trends of three major crops’ yields in Japan

Long-term trend of crop yields has been widely studied in global scales to find which crops and which geographic regions offer the best hope of meeting food demands, and which regions needed the most improvements. In this study, a mathematical method was applied to analyze spatial patterns in long-term temporal trends of three major crops’ yields in Japan archipelago. The changes in annual yields of rice, wheat, and soybean over a period of about 60 years in all 47 prefectures of Japan was analyzed by using the data of agricultural records. For all the three crops, the nationwide yields previously improved, but currently were stagnating in Japan. The result suggests that the annual yields were not improving in 53, 85, and 89% of those prefectures in Japan for rice, wheat, and soybean, respectively. The spatial patterns in temporal trends show that the percentage of number of yield-not-improving prefecture was higher in low latitude regions than high latitude regions. These results highlight the increasingly difficult challenge of meeting the growing demands and stagnating supplies in daily staple foods not only for agricultural scientists but also for Japanese society.

Detection of Precipitation and Temperature Trend Patterns for Mulanje District, Southern Part of Malawi

This paper reports on the recent trend of climate changes taking place in Mulanje, one of the major tea producing districts in Malawi, with a focus on precipitation and temperature. The study analyzed trends in weather time series (air temperature and rainfall) data from 1980 to 2011 using non-parametric Mann-Kendall test. The MK test gave a mix of positive and negative trends for the monthly and seasonal precipitation. Only the month of December showed a positive significant trend while February, April, May, June September, October and November had a significant negative trend. Cool and wet as well as hot and dry season showed a significant decreasing trend in precipitation. The data also indicated that annual rainfall variation is decreasing with time at the rate of about 4.29 mm per year while temperature is increasing by about 0.04°C every year. The MK test for temperature results showed a significant increasing trend for both mean annual minimum and maximum temperature. The results of the study do not deny climate change is happening, therefore, proper adaptation measures should be employed to reduce the vulnerability to climate change in the area because of its economic significance in tea production.

Trends in extreme temperature over Nigeria from percentile‐based threshold indices

ABSTRACTThis study analyses spatial and temporal trends in extreme temperature indices over Nigeria. The percentile indices were calculated from newly homogenized daily minimum and maximum temperature data for the period 1971–2012 for 21 stations in Nigeria. Indices describing the characteristics of hot extremes and cold extremes are calculated with the RClimDex software. The annual and seasonal trends in these indices are obtained using the ordinary least square fit and the statistical significance tested using the R‐based modified Mann–Kendall test. We examine characteristics of these indices for the entire country and separately for the three geographical zones in Nigeria: Guinea, Savanna, and Sahel. The spatial and temporal patterns of trends in the indices indicate that Nigeria has experienced statistically significant increase in the frequency of hot extreme and decrease in cold extreme events. Although majority of the stations have significant trends in warm days and warm nights, the annual trend is greatest in warm nights. In addition, the rate of warming in minimum temperature (‘warm nights’) is stronger in June, July, August (JJA) and September, October, November (SON) compare with December, January, February (DJF) and March, April, May (MAM). As for the trends in cold days and cold nights, the trends in cold nights are larger than for cold days at both the annual and seasonal scales. The regional analysis indicates that trends in warm nights and cold nights are most pronounced in Guinea and Sahel regions.

Attribution of European precipitation and temperature trends to changes in synoptic circulation

Abstract. Surface climate in Europe is changing and patterns in trends have been found to vary at sub-seasonal scales. This study aims to contribute to a better understanding of these changes across space and time by analysing to what degree observed climatic trends can be attributed to changes in synoptic atmospheric circulation. The relative importance of synoptic circulation changes (i.e. trends in synoptic type frequencies) as opposed to trends in the hydrothermal properties of synoptic types (within-type trends) on precipitation and temperature trends in Europe is assessed on a monthly basis. The study is based on mapping spatial and temporal trend patterns and their variability at a relatively high resolution (0.5° × 0.5°; monthly) across Europe. Gridded precipitation and temperature data (1963–2001) originate from the Watch Forcing Data set and synoptic types are defined by the objective SynopVis Grosswetterlagen (SVG). During the study period, relatively high influence of synoptic circulation changes are found from January to March, contributing to wetting trends in northern Europe and drying in the south. Simultaneously, particularly dry synoptic types get warmer first in south-western Europe in November and/or December and affect most of Europe in March and/or April. Strong influence of synoptic circulation changes is again found in June and August. In general, changes in synoptic circulation has a stronger effect on climate trends in north-western Europe than in the south-east. The exact locations of the strongest influence of synoptic circulation changes vary with the time of year and to some degree between precipitation and temperature. Throughout the year and across the whole of Europe, precipitation and temperature trends are caused by a combination of synoptic circulation changes and within-type changes with their relative influence varying between regions, months and climate variables.

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010

PurposeTo demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010. MethodsWe applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty. ResultsThe average local percent change in heart disease mortality from each method was −4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0–0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods. ConclusionsThe similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates.