Weather Time Scales Research Articles

Sub-seasonal prediction skill: is the mean state a good model evaluation metric?

Evaluating forecast models encompasses assessing their ability to accurately depict observed climate states and predict future climate variables. Various evaluation methods, from computationally efficient measures like the anomaly correlation coefficient to more intricate approaches, have been formulated. While simpler methods provide limited information, climatology, due to its simplicity and immediate linkage to model performance, is a commonly utilized primary evaluation metric. In this study focusing on temperature and precipitation, we propose a novel metric based on the model’s mean state, integrating both climatology and the seasonal cycle for a more accurate assessment of the relationship between mean state performance and prediction skill on weather and sub-seasonal time scales compared to relying solely on climatology. This integrated metric reveals a robust correlation between temperature and precipitation across diverse geographical locations, with a more pronounced effect in tropical areas when considering the seasonal cycle. Additionally, we find that temperature exhibits higher prediction skill compared to precipitation. The discovered relationship serves as a potential early indicator for predicting the efficacy of Seasonal to Sub-seasonal (S2S) models and offers valuable insights for model development, emphasizing the significance of this integrated metric in enhancing S2S model performance and advancing climate prediction capabilities.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Disentangling the solar activity–solar wind predictive causality at Space Climate scales

The variability in the magnetic activity of the Sun is the main source of the observed changes in the plasma and electromagnetic environments within the heliosphere. The primary way in which solar activity affects the Earth’s environment is via the solar wind and its transients. However, the relationship between solar activity and solar wind is not the same at the Space Weather and Space Climate time scales. In this work, we investigate this relationship exploiting five solar cycles data of Ca II K index and solar wind parameters, by taking advantage of the Hilbert–Huang Transform, which allows to separate the contribution at the different time scales. By filtering out the high-frequency components and looking at decennial time scales, we confirm the presence of a delayed response of solar wind to Ca II K index variations, with a time lag of ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 3.1-year for the speed and ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 3.4-year for the dynamic pressure. To assess the results in a stronger framework, we make use of a Transfer Entropy approach to investigate the information flow between the quantities and to test the causality of the relation. The time lag results from the latter are consistent with the cross-correlation ones, pointing out the presence of a statistical significant information flow from Ca II K index to solar wind dynamic pressure that peaks at time lag of 3.6-year. Such a result could be of relevance to build up a predictive model in a Space Climate context.

Costs and benefits of climate services for heat-health adaptation in Europe

Abstract Background Climate services for health aim to support informed decision-making and enable early action and preparedness. More knowledge is still needed regarding the costs and benefits of climate services, as well as effectiveness, and technical, structural, and societal barriers and facilitators to implementation and use. Aim To map the evidence on the economic assessment and use of climate services for health in reducing heat-associated mortality and morbidity and explore the societal impact of climate services, particularly in Europe. Methods We focus on two methods in this study: 1) a scoping review of literature on the economic assessment and health benefit of climate services 2) a set of interviews with climate service developers and providers in Europe to understand further technical and societal aspects as well as evaluation of such services. Findings From the scoping review, a total of 27 peer-reviewed articles all presenting heat early warning systems (HEWS) that work on a weather timescale were identified. The results from economic assessment studies suggested HEWS can produce health benefits greater than the operational cost, even during future climate change scenarios. While most of the studies assessing health outcomes have suggested that HEWS can help in reducing heat-related mortality and morbidity, some concluded no significant impact and some suggested increased healthcare services utilization after implementation of HEWS. The findings from the six interviews conducted across Europe identifies barriers for climate service development and implementation such as insufficient financing and cultural differences between climate and health professionals. Conclusions Our results find limited evidence on the economic assessment of climate services, gap in knowledge regarding health benefits from climate services using seasonal/sub-seasonal and long-term climate information and several barriers to making these services useful for end-users.

Are water bodies effective for urban heat mitigation? Evidence from field studies of urban lakes in two humid subtropical cities

Blue infrastructure is considered a nature-based solution to address urban heat problems. However, the potential nocturnal urban heat island (UHI) effect of water bodies has not been investigated and elaborated thoroughly. Besides, existing studies have primarily focused on surface temperature and are limited to individual cities during summer daytime. In this study, screen-height air temperature/humidity measurements at lake, residential, and rural sites in two humid subtropical cities of China (Nanjing, Guangzhou) were conducted continuously for one year. The UHI intensity (UHII), urban moisture island intensity (UMII, evaluated by humidity ratio difference), and heat index (HI) were analyzed across different weather conditions and time scales. Results demonstrated that (1) compared to residential sites, urban lakes can help mitigate urban heat excess, while this capability at night is not as significant as during daytime; compared to rural sites, urban lakes exhibit diurnal thermal behavior of daytime cooling and nighttime warming, with a weak urban cool island intensity (UCII) of 0.1–0.6 °C during the day but a significant UHII of 1.2–1.3 °C at night during warm months (May–September), which is comparable to residential sites with UHII of 1.1–1.3 °C; (2) urban moisture excess tends to occur during nighttime and cold seasons; (3) compared to rural sites, lakes contribute to a good relief on heat stress during daytime, but a significant negative effect at night due to the intensified warming and humidifying. Findings of this study can provide empirical evidence to help better understand the effectiveness of water bodies for urban heat mitigation.

Anthropogenic fingerprints in daily precipitation revealed by deep learning

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1–4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

Public Information Priorities across Weather Hazards and Time Scales

Abstract As the abundance of weather forecast guidance continues to grow, communicators will have to prioritize what types of information to pass on to decision-makers. This work aims to evaluate how members of the public prioritize weather forecast attributes (including information about location, timing, chance, severity, impacts, and protective actions) on average and across event timelines in the severe, tropical, and winter weather domains. Data from three demographically representative surveys of U.S. adults indicate that members of the public generally prioritize information about event location, timing, and severity when evaluating the importance of forecast attributes. This pattern is largely consistent across hazard domains but varies across event timelines. In early stages of a forecast (such as the outlook time scale), people generally prioritize information about chance and location. In middle stages (watch time scale), event timing and severity become more important. In late stages (warning time scale), information about protective actions is a higher priority, especially for people with less exposure to a hazard.

A 258-year-long data set of temperature and precipitation fields for Switzerland since 1763

Abstract. Climate reconstructions give insights in monthly and seasonal climate variability in the past few hundred years. However, for understanding past extreme weather events and for relating them to impacts, for example through crop yield simulations or hydrological modelling, reconstructions on a weather timescale are needed. Here, we present a data set of 258 years of daily temperature and precipitation fields for Switzerland from 1763 to 2020. The data set was reconstructed with the analogue resampling method, which resamples meteorological fields for a historical period based on the most similar day in a reference period. These fields are subsequently improved with data assimilation for temperature and bias correction for precipitation. Even for an early period prior to 1800 with scarce data availability, we found good validation results for the temperature reconstruction especially in the Swiss Plateau. For the precipitation reconstruction, skills are considerably lower, which can be related to the few precipitation measurements available and the heterogeneous nature of precipitation. By means of a case study of the wet and cold years from 1769 to 1772, which triggered widespread famine across Europe, we show that this data set allows more detailed analyses than hitherto possible.

Subseasonal controls of U.S. landfalling tropical cyclones

Landfalling tropical cyclones (LTCs) are the most devastating disaster to affect the U.S., while the demonstration of skillful subseasonal (between 10 days and one season) prediction of LTCs is less promising. Understanding the mechanisms governing the subseasonal variation of TC activity is fundamental to improving its forecast, which is of critical interest to decision-makers and the insurance industry. This work reveals three localized atmospheric circulation modes with significant 10–30 days subseasonal variations: Piedmont Oscillation (PO), Great America Dipole (GAD), and the Subtropical High ridge (SHR) modes. These modes strongly modulate precipitation, TC genesis, intensity, track, and landfall near the U.S. coast. Compared to their strong negative phases, the U.S. East Coast has 19 times more LTCs during the strong positive phases of PO, and the Gulf Coast experiences 4–12 times more frequent LTCs during the positive phases of GAD and SHR. Results from the GFDL SPEAR model show a skillful prediction of 13, 9, and 22 days for these three modes, respectively. Our findings are expected to benefit the prediction of LTCs on weather timescale and also suggest opportunities exist for subseasonal predictions of LTCs and their associated heavy rainfalls.

How Stable Water Isotope Measurements and Modeling Can Help Bridge the Gap between Research on Weather and Climate Time Scales

How Stable Water Isotope Measurements and Modeling Can Help Bridge the Gap between Research on Weather and Climate Time Scales

Global decline in ocean memory over the 21st century.

Ocean memory, the persistence of ocean conditions, is a major source of predictability in the climate system beyond weather time scales. We show that ocean memory, as measured by the year-to-year persistence of sea surface temperature anomalies, is projected to steadily decline in the coming decades over much of the globe. This global decline in ocean memory is predominantly driven by shoaling of the upper-ocean mixed layer depth in response to global surface warming, while thermodynamic and dynamic feedbacks can contribute substantially regionally. As the mixed layer depth shoals, stochastic forcing becomes more effective in driving sea surface temperature anomalies, increasing high-frequency noise at the expense of persistent signals. Reduced ocean memory results in shorter lead times of skillful persistence-based predictions of sea surface thermal conditions, which may present previously unknown challenges for predicting climate extremes and managing marine biological resources under climate change.

Ultra-high resolution δ18O of land snail shell: A potential tool to reconstruct frequency and intensity of paleoprecipitation events

Extreme rainfall events cause tremendous threats to people’s lives and economic well-being. Given the short time span of instrumental records, reconstructing the full range of past rainfall variability using available proxies is impossible when trying to unravel the history and underlying mechanisms of extreme precipitation events. However, this work is usually limited by the temporal resolution of current paleoclimate records. Here we demonstrate that the ultra-high resolution measurements of δ18O of terrestrial snail shells of Cathaica fasciola (Draparnaud, 1801) can be used to reconstruct the rainfall variability on weather time scales. Broadly, the modern daily time resolution snail shell’s δ18O profile shows a negative trend from late March to September in 2020, which resembles the variations of monitored δ18O and amount of precipitation. Meanwhile, six abrupt negative deviations were observed in the δ18O profiles and these deviations matched well with the instrumental precipitation events for the same period. The high correlation of shell’s δ18O with rainfall amount during the six precipitation events indicates that the ultra-high resolution δ18O records of gastropod shells have the potential to qualitatively record local synoptic rainfall events. Our study highlights that the terrestrial gastropod shell could provide daily to weekly geochemical proxies and has the potential to be used as an ultra-high resolution archive for characterizing terrestrial paleoweather, such as extreme precipitation events.

Monotonic Increase of Extreme Precipitation Intensity With Temperature When Controlled for Saturation Deficit

AbstractPast studies based on univariate scaling analyses at the weather time scale documented a negative scaling of extreme precipitation intensity (EPI), which prevents EPI extrapolation from past climate. Here we present a bivariate scaling analysis and show that, contrary to the univariate scaling results, EPI monotonically increases with temperature and shows no negative scaling when controlled for saturation deficit. The observed EPI‐temperature relationship in saturated atmosphere is surprisingly similar among different regions and closely follows the Clausius‐Clapeyron scaling; climate models produce greater regional dependence of the scaling relationship with a wide range of scaling rate. For extratropical regions, the model‐simulated EPI‐temperature relationship under saturation shows a past‐to‐future continuity, which could potentially support extrapolation to a warmer climate. The scaling at saturation bridges the EPI‐temperature relationship between weather and climate time scales and may enable potential prediction of future precipitation extremes via extrapolation from past observations.

Has substantial sea ice loss along the Siberian coast contributed to the 2020/2021 winter cold wave in China?

In winter 2020/2021, several episodes of cold air outbreaks have wreaked havoc across much of China, resulting in exceptionally low temperatures over northern-to-southeastern China. Our analysis shows that the anomalous circulation leading to the cold wave in China was characterized as: (a) a strengthening of the Siberian high, (b) a deepening of the East Asian trough, and (c) a weakening of the stratospheric polar vortex. Prior to the winter, in summer and autumn, the largest sea ice loss along the Siberian coast was observed. We therefore address the question whether the substantial sea ice loss has contributed to the cold wave. First, applying singular value decomposition (SVD) analysis, we find strong coherence between the autumn Arctic sea ice coverage and temperatures over the central-to-east Asian region with the sea ice component (SVD1_SIC) reaching a record high in 2020 and displaying a decadal shift around 2005. Associated with both the interannual and decadal variations of the SVD1_SIC, there are similar circulation anomalies as described in (a), (b) and (c) above. Second, applying self-organizing maps, wintertime daily air temperatures are clustered into nine nodes with Node-1 mostly resembling “warm Arctic, cold Eurasia” pattern. The frequency of Node-1 shows a similar decadal shift as observed in SVD_SIC1 above, with a correlation of about 0.56. Associated with Node-1, the circulation anomalies are consistent with features highlighted in (a), (b) and (c) above. Thus, taking all the evidence into account, we conclude that the substantial sea ice loss along the Siberian coast contributed significantly to the 2020/2021 winter cold wave in China. The mechanism behind the contribution in this study highlights that the Arctic sea ice loss is favourable for certain circulation patterns on climate timescale, reinforcing disruptive chain of reactions on weather timescale, and leading to severe cold events.

Effect of CO2 on biogeochemical reactions and microbial community composition in bioreactors with deep groundwater and basalt

Changes in subsurface microbiology following CO2 injection have the potential to impact carbon trapping in CO2 storage reservoirs. However, much remains to be learned about responses of natural microbial consortia to elevated CO2 in basaltic systems. This study asks: how will microbes from deep (700 m) groundwater change along a gradient in CO2 (0–20 psi) in batch reactor systems containing basalt chips and groundwater amended with lactate? Reactors incubated for 87 days at 23 °C. Results for reactors with low CO2 (0 and 3 psi) differed considerably from those with high CO2 (10 and 20 psi). In reactors with low CO2, pH was >6.5 and lactate started to be used within 24 days. By 40 days, lactate was completely consumed and acetate increased to ~4 mM. As lactate was consumed, sulfate decreased from 0.16 to 0 mM after 40 days. In contrast, in reactors with high CO2, pH was <6.5, lactate and sulfate concentrations varied little and acetate was not produced. Biogeochemical modeling and community analyses indicate that differences between reactors with low and high CO2 reflect tolerances of reactor microbes to CO2 exposure. Communities in the low CO2 reactors carried out syntrophic lactate oxidation coupled with methanogenesis and sulfate reduction. Bacteroidota and Firmicutes became dominant phyla after 24 days and groups capable of sulfate reduction and methanogenesis were detected. In reactors with high CO2, however, biogeochemical activity was insignificant, no groups capable of sulfate reducion or methanogenesis were observed, and the community became less diverse during the incubation. These findings show that the response of microbial consortia can vary sharply along a CO2 gradient, creating significant differences in community composition and biogeochemistry, and that the timescale of basalt weathering is likely not rapid enough to prevent significant stress following a rapid increase in CO2 abundance.

Seasonal Performance of a Nonhydrostatic Global Atmospheric Model on a Cubed‐Sphere Grid

AbstractThe Korean Integrated Model (KIM), a recently developed nonhydrostatic global atmospheric model over a cubed‐sphere grid, was deployed in April 2020 as an operational weather forecasting model. As its application extends to research and predictions longer than the weather time scale, we evaluated the ability of the KIM on seasonal ensemble simulation for the boreal winter and summer cases with respect to seasonal mean biases. The results are compared with those obtained from a conventional hydrostatic spectral model, which has been widely used for seasonal simulations and in climate research. To isolate the origin of the error sources, the same physics packages is used in both the KIM and the reference models. The simulated mean states are very close to the reanalysis for the selected cases. Most large‐scale fields from the KIM are comparable to those from the reference model, which implies that the general features of large‐scale variables and precipitation are highly governed by physical parameterizations, and that the physics‐dynamics coupling is stable in a long‐term simulation. Large‐scale tropical circulations, such as the Hadley and Walker circulations, need to be improved for applications related to future changes and climate projections. Moreover, the results reveal that the simulated global precipitation band is misplaced and the heat fluxes over oceans are relatively misrepresented near the eastern boundaries of tropical and subtropical regions. This analysis suggests the necessity of realistic atmosphere‐ocean interactions that reflect ocean overturning circulation via ocean coupling as well as the refinement of deep and shallow convection schemes.

Extended Range Arctic Sea Ice Forecast with Convolutional Long-Short Term Memory Networks

AbstractOperational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time-scales. Numerical models require near real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely Convolutional Long Short Term Memory Networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to sub-seasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is promising to enhance operational Arctic sea ice forecasting in the near future.

Improvements in tropical precipitation and sea surface air temperature fields in a coupled atmosphere–ocean data assimilation system

AbstractA coupled atmosphere–ocean data assimilation system, the Meteorological Research Institute‐Coupled Data Assimilation System Version 1 (MRI‐CDA1), was developed based on the coupled atmosphere–ocean general circulation model and separate atmosphere and ocean analysis routines operated by the Japan Meteorological Agency (JMA). To implement coupled atmosphere–ocean data assimilation, 6‐hr data assimilation cycles with the coupled model in the outer loop are adopted in atmospheric data assimilation, whereas incremental analysis updates with 10‐day data assimilation cycles are adopted in ocean data assimilation. A coupled data assimilation (reanalysis) experiment (CDA‐Exp) is conducted using MRI‐CDA1 along with an uncoupled reanalysis experiment (UCPL‐Exp) in which the same atmospheric component of the coupled model is forced by prescribed sea surface temperature (SST) similarly to the JMA reanalysis, JRA‐55. Climatological precipitation and variations in precipitation and sea surface air temperature (SAT) are better represented in CDA‐Exp than in JRA‐55, which is brought about by the modification of the atmosphere model physics of the coupled model to better represent climate states. In CDA‐Exp, the SST adjustment to the atmosphere amplifies the lead/lag correlations between subseasonal variations of SST and precipitation. The atmosphere–ocean coupling generates SST variations associated with tropical instability waves, and the SAT field responds to SST variations. The SST–precipitation and SST–SAT relationships on the weather timescale are also recovered in CDA‐Exp, although they are hardly seen in UCPL‐Exp. The coupled model physics generates weather‐timescale SST variations consistent with the atmospheric state, and the atmospheric parameters respond to the SST variations through the coupled model physics. This study suggests that the benefits of coupled data assimilation would be more evident if the ability of MRI‐CDA1 to represent SST variations and its interaction with the atmosphere are improved.

Revamping extended range forecast of Indian summer monsoon

Forecast of the Indian summer monsoon on an extended range (beyond the conventional one-week lead time) is critical for an agronomic economy like India. Although dynamic models have been quite successful in capturing and representing monsoon circulation, they fail to sustain this skill beyond the standard weather time scale (7–10 days). As such, the present study is directed at developing a computationally feasible, yet reliable method of statistical downscaling that further improves the present skill of global dynamic models for extended range forecasting. We quantitatively demonstrate the feasibility of this post-processing statistical module for improving the predictability of the dynamic Extended Range Prediction System (ERPAS), which is developed by Indian Institute of Tropical Meteorology, Pune, India and now operational in the country. It first develops climate cluster(s) (rainfall states in the present case), then builds a statistical relationship between these clusters and a set of appropriate climate variables using a robust and advanced classification technique known as Extreme Gradient Boosting (XGboost), and eventually delivers the real-valued precipitation at individual grid cells via a non-parametric regression. The module is able to skilfully capture the active and break phases of the Indian summer monsoon, and also subsequently project them for the ensuing regression component of the module. This approach shows to significantly boost the prediction skill over the Core Monsoon Zone of Indian mainland up to a lead time of 4 weeks. Our statistical downscaled model is comparable in the week 1 lead time but outperforms global models for 2nd, 3rd, and 4th weeks lead time. Nonetheless, this performance is retained only in the northern and central region, and not ubiquitous over the rest of India.

Slope Gradient Controls Soil Thickness and Chemical Weathering in Subtropical Brazil: Understanding Rates and Timescales of Regional Soilscape Evolution Through a Combination of Field Data and Modeling

AbstractSoil thickness and residence time are regulated by a dynamic interplay between soil formation and lateral transport of soil particles and solutes. To unravel this interplay and infer patterns and rates of chemical weathering, soil physical and chemical properties can be used. Here, we present an integrated approach combining numerical modeling with field measurements to assess the impact of slope gradient on soil thickness and chemical weathering at a regional scale. We first perform a number of synthetic model runs simulating soil formation, weathering, erosion, and deposition, which show that soil thickness and weathering degree decline with increasing slope gradient. We then evaluate how those functional relationships compare to soil‐landscape data observed in the field. Soils are sampled at 100 midslope positions under varying slope gradient. The weathering degree is determined using three chemical weathering indices: ratio of iron oxides to total iron (Fed/Fet), chemical index of alteration (CIA), and total reserve in bases (TRB). Finally, we calibrate the Be2D model to our field data to constrain soil residence times and chemical weathering rates. The modeled weathering rates decrease with increasing soil residence time and decreasing slope gradient. The application of the soil‐landscape evolution model in Southern Brazil shows that weathering rates can vary up to 2 orders of magnitude and depend on hillslope gradient. Notwithstanding model limitations and data uncertainties, we demonstrate the potential of an integrated approach, where field data and numerical modeling are integrated to unravel the timescale of soil weathering along transport over hillslopes.