Global reorganization of atmospheric circulation during Dansgaard-Oeschger cycles.

Ice core records from Greenland provide evidence for multiple abrupt cold-warm-cold events recurring at millennial time scales during the last glacial interval. Although climate variations resembling Dansgaard-Oeschger (DO) oscillations have been identified in climate archives across the globe, our understanding of the climate and ecosystem impacts of the Greenland warming events in lower latitudes remains incomplete. Here, we investigate the influence of DO-cold-to-warm transitions on the global atmospheric circulation pattern. We comprehensively analyze δ18O changes during DO transitions in a globally distributed dataset of speleothems and set those in context with simulations of a comprehensive high-resolution climate model featuring internal millennial-scale variations of similar magnitude. Across the globe, speleothem δ18O signals and model results indicate consistent large-scale changes in precipitation amount, moisture source, or seasonality of precipitation associated with the DO transitions, in agreement with northward shifts of the Hadley circulation. Furthermore, we identify a decreasing trend in the amplitude of DO transitions with increasing distances from the North Atlantic region. This provides quantitative observational evidence for previous suggestions of the North Atlantic region being the focal point for these archetypes of past abrupt climate changes.

Modes of Pangean lake level cyclicity driven by astronomical climate pacing modulated by continental position and pCO[Formula: see text].

Orbital cyclicity is a fundamental pacemaker of Earth's climate system. The Newark-Hartford Basin (NHB) lake sediment record of eastern North America contains compelling geologic expressions of this cyclicity, reflecting variations of climatic conditions in tropical Pangea during the Late Triassic and earliest Jurassic (~233 to 199 Ma). Climate modeling enables a deeper mechanistic understanding of Earth system modulation during this unique greenhouse and supercontinent period. We link major features of the NHB record to the combined climatic effects of orbital forcing, paleogeographic changes, and atmospheric pCO[Formula: see text] variations. An ensemble of transient, orbitally driven climate simulations is assessed for nine time slices, three atmospheric pCO[Formula: see text] values, and two paleogeographic reconstructions. Climatic transitions from tropical humid to more seasonal and ultimately semiarid are associated with tectonic drift of the NHB from [Formula: see text] to [Formula: see text]. The modeled orbital modulation of the precipitation-evaporation balance is most pronounced during the 220 to 200 Ma interval, whereas it is limited by weak seasonality and increasing aridity before and after this interval. Lower pCO[Formula: see text] at around 205 Ma contributes to drier climates and could have led to the observed damping of sediment cyclicity. Eccentricity-modulated precession dominates the orbitally driven climate response in the NHB region. High obliquity further amplifies summer precipitation through the seasonal shifts in the tropical rainfall belt. Regions with other proxy records are also assessed, providing guidance toward an integrated picture of global astronomical climate forcing in the Late Triassic and ultimately of other periods in Earth history.

Significant impact of forcing uncertainty in a large ensemble of climate model simulations.

Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.

Paleoclimate-conditioning reveals a North Africa land-atmosphere tipping point.

While paleoclimate records show that the Earth System is characterized by several different tipping points, their representation within Earth System models (ESMs) remains poorly constrained. This is because historical observations do not encompass variations large enough to provoke such regime changes, and paleoclimate conditions are rarely used to help develop and tune ESMs, which potentially ignores a rich source of information on abrupt climate change. A critical example is the early to mid-Holocene "greening" and subsequent rapid desertification of the Sahara, which most ESMs fail to reproduce, casting doubt on the representation of land-atmosphere coupling and monsoon dynamics. Here, we show that this greening and abrupt termination can be successfully simulated with one ESM after optimizing uncertain model components using both present-day observations and crucially mid-Holocene (6,000 y before present) reconstructions. The optimized model displays abrupt threshold behavior, which shows excellent agreement with long paleoclimate records that were not used in the original optimization. These results suggest that in order to realistically capture climate-system thresholds, ESMs first need to be conditioned with appropriate paleoclimate information.

Short-term variation in leaf-level water use efficiency in a tropical forest.

The representation of stomatal regulation of transpiration and CO2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (gs ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g1 (stomatal slope, inversely proportional to water use efficiency) and g0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf-level response curves of six tropical broadleaf evergreen tree species. g1 estimated from ex situ response curves averaged 50% less than g1 estimated from survey data. While g0 and g1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g1 and g0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled gs can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g1 will be required to account for this phenomenon in land-surface models.

Challenges simulating the AMOC in climate models.

The latest assessment report from the Intergovernmental Panel on Climate Change concluded that the Atlantic Meridional Overturning Circulation (AMOC) was very likely to decline over the twenty-first century under all emissions scenarios; however, there was low confidence in the magnitude of the decline. Recent research has highlighted that model biases in the mean climate state can affect the AMOC in its mean state, variability and its response to climate change. Hence, understanding and reducing these model biases is critical for reducing uncertainty in the future changes of the AMOC and in its impacts on the wider climate. We discuss how model biases, in particular salinity biases, influence the AMOC and deep convection. We then focus on biases in the UK HadGEM3-GC3-1 climate model and how these biases change with resolution. We also discuss ongoing model development activities that affect these biases, and highlight priorities for improved representation of processes, such as the position of the North Atlantic Current, transports in narrow boundary current, resolution (or improved parameterization) of eddies and spurious numerical mixing in overflows. This article is part of a discussion meeting issue 'Atlantic overturning: new observations and challenges'.

Sub-regional variation in atmospheric and land variables regulates tea yield in the Dooars region of West Bengal, India.

Climatic variables can have localized variations within a region and these localized climate patterns can have significant effect on production of climate-sensitive crops such as tea. Even though tea cultivation and industries significantly contribute to employment generation and foreign earnings of several South Asian nations including India, sub-regional differences in the effects of climatic and soil variables on tea yield have remained unexplored since past studies focused on a tea-producing region as a whole and did not account for local agro-climatic conditions. Here, using a garden-level panel dataset based on tea gardens of Dooars region, a prominent tea-producing region in India, we explored how sub-regional variations in climatic and land variables might differently affect tea yield within a tea-producing region. Our analysis showed that the Dooars region harboured significant spatial variability for different climatic (temperature, precipitation, surface solar radiation) and soil temperature variables. Using graph-based Louvain clustering of tea gardens, we identified four spatial sub-regions which varied in terms of topography, annual and seasonal distribution of climatic and land variables and tea yield. Our sub-region-specific panel regression analyses revealed differential effects of climatic and land variables on tea yield of different sub-regions. Finally, for different emission scenario, we also projected future (2025-2100) tea yield in each sub-region based on predictions of climatic variables from three GCMs (MIROC5, CCSM4 and CESM1(CAM5)). A large variation in future seasonal production changes was projected across sub-regions (-23.4-35.7% changes in premonsoon, -4.2-3.1% changes in monsoon and -10.9-10.7% changes in postmonsoon tea production, respectively).

Coupling SWAT and DPSIR models for groundwater management in Mediterranean catchments.

Groundwater is an essential natural resource and has a significant role in human and environmental health as well as in the economy. Management of subsurface storage remains an important option to meet the combined demands of humans and ecosystems. The increasing need to find multi-purpose solutions to address water scarcity is a global challenge. Thus, the interactions leading to surface runoff and groundwater recharge have received particular attention over the last decades. Additionally, new methods are developed to incorporate the spatial-temporal variation of recharge in groundwater modeling. In this study, groundwater recharge was spatiotemporally quantified using the Soil and Water Assessment Tool (SWAT) in the Upper Volturno-Calore hydrological basin in Italy and the results were compared with other two basins in Greece (Anthemountas and Mouriki). SWAT model was applied in actual and future projections (2022-2040) using the Representative Concentration Pathway (RCP) 4.5 emissions scenario to evaluate changes in precipitation and assess the future hydrologic conditions, along with, the Driving Force-Pressure-State-Impact-Response (DPSIR) framework that was applied in all the basins as a low-cost analysis of integrated physical, social, natural, and economic factors. According to the results, no significant variations in runoff are predicted in the Upper Volturno-Calore basin for the period 2020-2040 while the potential evapotranspiration percentage varies from 50.1% to 74.3% and infiltration around 5%. The limited primary data constitutes the main pressure in all sites and exaggerates the uncertainty of future projections.

Performance evaluation of multiple regional climate models to simulate rainfall in the Central Rift Valley Lakes Basin of Ethiopia and their selection criteria for the best climate model.

The historical datasets of five regional climate models (RCMs) available in the Coordinated Regional Downscaling Experiment (CORDEX)-Africa database are evaluated against ground-based observed rainfall in the Central Rift Valley Lakes Basin of Ethiopia. The evaluation is aimed at determining how well the RCMs reproduce monthly, seasonal, and annual cycles of rainfall and quantify the uncertainty between the RCMs in downscaling the same global climate model outputs. Root mean square, bias, and correlation coefficient are used to evaluate the ability of the RCM output. The multicriteria decision method of compromise programming was used to choose the best climate models for the climate condition of the Central Rift Valley Lakes subbasin. The Rossby Center Regional Atmospheric Model (RCA4) has downscaled ten global climate models (GCMs) and reproduces the monthly rainfall with a complex spatial distribution of bias and root mean square errors. The monthly bias varies in the range of - 35.8 to 189%. The summer (wet), spring, winter (dry), and annual rainfall varied within the range of 1.44 to 23.66%, - 7.08 to 20.04%, - 7.35 to 57%, and - 3.11 to 16.5%, respectively. To find the source of uncertainty, the same GCMs but downscaled by different RCMs were analyzed. The test results showed that each RCM differently downscaled the same GCM, and there was no single RCM model that consistently simulated the climate conditions over the stations in the study regions. However, the evaluation finds reasonable model skill in representing the temporal cycles of rainfall and suggests the use of RCMs where climate data is scarce after bias correction.

Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss.

Anthropogenic global warming is redistributing marine life and may threaten tropical benthic invertebrates with several potential extinction mechanisms. The net impact of climate change on geographical extinction risk nevertheless remains uncertain. Evidence of widespread climate-driven extinctions and of potentially unidentified mechanisms exists in the fossil record. We quantify organism extinction risk across thermal habitats, estimated by paleoclimate reconstructions, over the past 300 million years. Extinction patterns at seven known events of rapid global warming (hyperthermals) differ significantly from typical patterns, resembling those driven by global geometry under simulated global warming. As isotherms move poleward with warming, the interaction between the geometry of the globe and the temperature-latitude relationship causes an uneven loss of thermal habitat and a bimodal latitudinal distribution of extinctions. Genera with thermal optima warmer than ~21°C show raised extinction odds, while extinction odds continually increase for genera with optima below ~11°C. Genera preferring intermediate temperatures generally have no additional extinction risk during hyperthermals, except under extreme conditions as the end-Permian mass extinction. Widespread present-day climate-driven range shifts indicate that occupancy loss is already underway. Given the most-likely projections of modern warming, our model, validated by seven past hyperthermal events, indicates that sustained warming has the potential to annihilate cold-water habitat and its endemic species completely within centuries.

Dependencies of the indoor climate on the course of the seasons and derivation of regressions from long-term measurements.

A building's indoor climate is an essential input variable for a variety of building physics computational models, simulations, and analyses. Precise knowledge of the indoor climate is necessary to minimize the risk of mold or moisture damage and is required to ensure minimum heat insulation standards in buildings. Detailed data are especially necessary for the progressive application of transient calculations, for example, concerning thermal comfort or energy consumption. While the properties of building materials and the (local) outdoor climate are known, only rudimentary information about the dynamic indoor climate is available. Most existing information in the literature about indoor climate is fairly general and forgoes a differentiation between climatic region, occupancy profile, and the utilization of rooms. In this paper, we report on indoor climate measurements in naturally ventilated apartments over a period of 1 year. The measurement results complement the existing data to provide accurate indoor climate data in buildings. The measured values of indoor temperature and relative humidity serve to derive the dew point temperature and moisture load whereby dynamic time-dependent regression functions are determined for these parameters. The evaluations are carried out separately according to room use. The comparison of living rooms and bedrooms indicates a great influence of room use on the indoor climate in residential buildings. The determined indoor climate model can be used for the planning of buildings and simulations. The classification into living rooms and bedrooms makes it possible to take user behavior into account more realistically in building physics simulations. The minimum thermal insulation in residential buildings can also be checked and designed based on realistic data. The prediction interval describes the limits in which residential rooms are free of damage with a high probability. In this way, the indoor climate model describes an approach to examine and evaluate simulation results regarding condensation risk and mold damage in naturally ventilated rooms.

Slowdown of the Walker circulation at solar cycle maximum.

The Pacific Walker Circulation (PWC) fluctuates on interannual and multidecadal timescales under the influence of internal variability and external forcings. Here, we provide observational evidence that the 11-y solar cycle (SC) affects the PWC on decadal timescales. We observe a robust reduction of east-west sea-level pressure gradients over the Indo-Pacific Ocean during solar maxima and the following 1-2 y. This reduction is associated with westerly wind anomalies at the surface and throughout the equatorial troposphere in the western/central Pacific paired with an eastward shift of convective precipitation that brings more rainfall to the central Pacific. We show that this is initiated by a thermodynamical response of the global hydrological cycle to surface warming, further amplified by atmosphere-ocean coupling, leading to larger positive ocean temperature anomalies in the equatorial Pacific than expected from simple radiative forcing considerations. The observed solar modulation of the PWC is supported by a set of coupled ocean-atmosphere climate model simulations forced only by SC irradiance variations. We highlight the importance of a muted hydrology mechanism that acts to weaken the PWC. Demonstration of this mechanism acting on the 11-y SC timescale adds confidence in model predictions that the same mechanism also weakens the PWC under increasing greenhouse gas forcing.

Can nonlinear agrometeorological models estimate coffee foliation?

BACKGROUND: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987-2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four-parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees-day for 70 locations. RESULTS: The general model calibrated to estimate the arabica coffee foliation was accurate (mean absolute percentage error = 2.19%) and precise (R2 adj  = 0.99) and can be used to assist decision-making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax  = 97.63%, ymin  = 9%, Xo  = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION: Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of -0.94. Therefore, coffee foliation can be estimated using degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer. © 2021 Society of Chemical Industry.

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0.

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.

Impact of future climate scenarios on peatland and constructed wetland water quality: A mesocosm experiment within climate chambers.

Water purification is one of the most essential services provided by wetlands. A lot of concerns regarding wetlands subjected to climate change relate to their susceptibility to hydrological change and the increase in temperature as a result of global warming. A warmer condition may accelerate the rate of decomposition and release of nutrients, which can be exported downstream and cause serious ecological challenges; e.g., eutrophication and acidification. The aim of this study is to investigate the effect of climate change on water quality in peatland and constructed wetland ecosystems subject to water level management. For this purpose, the authors simulated the current climate scenario base on the database from Malmö station (Scania, Sweden) for 2016 and 2017 as well as the future climate scenarios for the last 30 years of the century based on the Representative Concentration Pathway (RCP) and different regional climate models (RCM) for a region wider than Scania County. For future climate change, the authors simulated low (RCP 2.6), moderate (RCP 4.5) and extreme (RCP 8.5) climate scenarios. All simulations were conducted within climate chambers for experimental peatland and constructed wetland mesocosms. Our results demonstrate that the effect of climate scenario is significantly different for peatlands and constructed wetlands (interactive effect) for the combined chemical variables. The warmest climate scenario RCP 8.5 is linked to a higher water purification function for constructed wetlands, but to a lower water purification function and a subsequent deterioration of peatland water qualities, even if subjected to water level management. The explanation for the different response of constructed wetlands and peatlands to climate change could be due to the fact that the substrate in the constructed wetland mesocosms and peatlands was different in terms of the organic matter quality and quantity. The utilization of nutrients by the plants and microbial community readily exceed the mineralization under a limited nutrient content (as we had in constructed wetland) when the temperature rises. However, concerning the extreme scenario RCP 8.5, the peatlands have shown a tendency to have reverse processes.

Global long-range transport and lung cancer risk from polycyclic aromatic hydrocarbons shielded by coatings of organic aerosol.

Polycyclic aromatic hydrocarbons (PAHs) have toxic impacts on humans and ecosystems. One of the most carcinogenic PAHs, benzo(a)pyrene (BaP), is efficiently bound to and transported with atmospheric particles. Laboratory measurements show that particle-bound BaP degrades in a few hours by heterogeneous reaction with ozone, yet field observations indicate BaP persists much longer in the atmosphere, and some previous chemical transport modeling studies have ignored heterogeneous oxidation of BaP to bring model predictions into better agreement with field observations. We attribute this unexplained discrepancy to the shielding of BaP from oxidation by coatings of viscous organic aerosol (OA). Accounting for this OA viscosity-dependent shielding, which varies with temperature and humidity, in a global climate/chemistry model brings model predictions into much better agreement with BaP measurements, and demonstrates stronger long-range transport, greater deposition fluxes, and substantially elevated lung cancer risk from PAHs. Model results indicate that the OA coating is more effective in shielding BaP in the middle/high latitudes compared with the tropics because of differences in OA properties (semisolid when cool/dry vs. liquid-like when warm/humid). Faster chemical degradation of BaP in the tropics leads to higher concentrations of BaP oxidation products over the tropics compared with higher latitudes. This study has profound implications demonstrating that OA strongly modulates the atmospheric persistence of PAHs and their cancer risks.

A novel method for assessing climate change impacts in ecotron experiments.

Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change.

Multi-model ensembles in climate science: Mathematical structures and expert judgements.

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: "How can ensemble studies be designed so that they probe uncertainty in desired ways?" This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by "plugging and playing" with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs.

The Sensitivity of Euro-Atlantic Regimes to Model Horizontal Resolution.

There is growing evidence that the atmospheric dynamics of the Euro-Atlantic sector during winter is driven in part by the presence of quasi-persistent regimes. However, general circulation models typically struggle to simulate these with, for example, an overly weakly persistent blocking regime. Previous studies have showed that increased horizontal resolution can improve the regime structure of a model but have so far only considered a single model with only one ensemble member at each resolution, leaving open the possibility that this may be either coincidental or model dependent. We show that the improvement in regime structure due to increased resolution is robust across multiple models with multiple ensemble members. However, while the high-resolution models have notably more tightly clustered data, other aspects of the regimes may not necessarily improve and are also subject to a large amount of sampling variability that typically requires at least three ensemble members to surmount.

Detection of climate change-driven trends in phytoplankton phenology.

The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5 days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long-term trends. We find that bloom timing generally shifts later at mid-latitudes and earlier at high and low latitudes by ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long-term trends in bloom timing, data at a temporal resolution of 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.