Precipitation in Mexico decreased to 630.14 mm in 2023 from 748.92 mm in 2022. This dataset includes a chart with historical data for Mexico Average Precipitation.

Mexico: Precipitation, mm per year: The latest value from 2021 is 758 mm per year, unchanged from 758 mm per year in 2020. In comparison, the world average is 1168 mm per year, based on data from 178 countries. Historically, the average for Mexico from 1962 to 2021 is 758 mm per year. The minimum value, 752 mm per year, was reached in 2000 while the maximum of 758 mm per year was recorded in 1962.

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a satellite-based precipitation dataset for hydrological and climate studies, spanning from 1983 to present. It is the longest satellite-based precipitation record available, with daily data at 0.25° resolution for the 60°S–60°N latitude band.PERSIANN rain rate estimates are generated at 0.25° resolution and calibrated to a monthly merged in-situ and satellite product from the Global Precipitation Climatology Project (GPCP). The model uses Gridded Satellite (GridSat-B1) infrared data at 3-hourly time steps, with the raw output (PERSIANN-B1) bias-corrected and accumulated to produce the daily PERSIANN-CDR.The maps show 31 years (1984–2014) of annual and seasonal median and interquartile range (IQR) data. The median represents the 50th percentile of precipitation, and the IQR reflects the range between the 75th and 25th percentiles, showing data variability. Median and IQR are preferred over mean and standard deviation as they are less influenced by extreme values and better represent non-normally distributed data, such as precipitation, which is skewed and zero-limited.Data and Metadata: NCEIThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.

The Advanced Weather Interactive Processing System (AWIPS) uses shapefiles for base maps in the system. These shapefiles contain boundaries of areas used by NWS for forecasts and warnings as well as map backgrounds.NWS BordersThe County Warning Area boundaries are the counties/zones for which each Weather Forecast Office (WFO) is responsible for issuing forecasts and warnings. The shapefile was created by aggregating public zones with the same CWA designation into a single polygon and manually adjusting the boundaries of the exceptions to the rule.The NWS county and state borders are background map used internally in NWS.Coastal Marine Zone ForecastThis map layer contains links to NWS marine weather forecasts for coastal or nearshore waters within 20nm of shore out to Day 5. It includes predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas or combined seas, and icing. Air temperature forecasts are optional. The forecasts will also include any marine weather advisories, watches, and/or warnings. The purpose of the forecasts is to support and promote safe transportation across the coastal waters. The forecasts are issued twice per day with updates as necessary by NWS Weather Forecast Offices (WFOs) along the coast and Great Lakes.Offshore Zone ForecastsThis map layer contains links to NWS marine weather forecasts for offshore waters beyond 20 or 30nm of shore out to Day 5. The forecast provides information to mariners who travel on the oceanic waters adjacent to the U.S., its territorial coastal waters and the Caribbean Sea. The forecasts include predictions on the likelihood of precipitation and/or reduced visibility, surface wind direction and speed, seas and likelihood of icing out to Day 5 along with information about any warnings. The offshore forecasts for the Western North Atlantic and Eastern North Pacific Oceans are produced by NWS/NCEP's Ocean Prediction Center. The offshore forecasts for the Gulf of Mexico and Caribbean Sea are issued by the NWS/NCEP National Hurricane Center's Tropical Analysis and Forecast Branch (TAFB). OPC and NHC/TAFB issues the forecasts four times daily at regular intervals, with updates when necessary. The offshore forecast for the waters around Hawaii are issued by the NWS Weather Forecast Office in Honolulu, HI four times daily at regular intervals, with updates when necessary. The offshore forecasts for Alaska waters in the Bering Sea and Gulf of Alaska are issued by NWS Weather Forecast Offices in Alaska at least twice a day with updates as necessary. The WFOs in Alaska include WFO Anchorage, WFO Fairbanks, and WFO Juneau.Public Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS surface weather forecasts, a zone-type forecast providing the average forecast conditions across the zone, usually at the county-scale or sub-county scale. These text forecasts include predictions of weather, sky cover, maximum and minimum surface air temperatures, surface wind direction and speed, and probability of precipitation out to 7 days into the future. In addition, the forecast highlights at the top include any active weather advisories, watches, and/or warnings. These zone predictions are derived from gridded forecasts created by NWS Weather Forecast Offices throughout the U.S. The text weather forecasts are usually issued in the early morning (e.g. 4AM LT) and early evening (4PM LT). They are updated during late mornings and late night and during fast changing weather conditions.Fire Weather Zone ForecastsThis layer includes links to NWS web pages posting the latest NWS Fire Weather Planning Forecasts, a zone-type forecast providing the average fire weather conditions across the zone. According to the NWS, the forecast is "used by land management personnel primarily for input in decision-making related to pre-suppression and other planning." The forecast is valid from the time of issuance through day five and sometimes through day seven and usually has a minimum of three 12-hour time periods. The forecast will have included a discussion of weather patterns affecting the forecast zone or area, identification of any active fire weather watches/warnings and a table of predicted fire weather variables for the next two days: 1) sky/weather conditions, 2) max/min air temperatures, 3) max/min relative humidity, 4) 0-minute average wind direction/speed at 20 feet and sometimes at another height (e.g. 10,000, 15,000 ft), 5) precipitation amount, duration, and timing, 6) mixing height, 7) transport winds, 8) vent category, and 9) several fire weather indices such as Haines Index, Lightning Activity (LAL), Chance of Wetting Rainfall (CWR), Dispersion Index, Low Visibility Occurrence Risk Index (LVORI), and Max LVORI. In addition, it will usually have a forecast in plain text for days 3 to 7. Sometimes an optional outlook of expected conditions for day 6 or possibly for day 6 and 7 is expected. The forecasts are issued by NWS WFOs at least once daily during the local fire season.Metadata:CWA: https://www.weather.gov/gis/CWAmetadataCoastal Marine: https://www.weather.gov/gis/CoastalMarineMetadataOffshore: https://www.weather.gov/gis/OffshoreZoneMetadataPublic Zones: https://www.weather.gov/gis/PublicZoneMetadataFire Zones: https://www.weather.gov/gis/FireZoneMetadataCounties: https://www.weather.gov/gis/CountyMetadataStates: https://www.weather.gov/gis/StateMetadataLink to data download: https://www.weather.gov/gis/AWIPSShapefilesQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This service is not time enabled

Climate change is acting on species and modifying communities and ecosystems through changes not only with respect to mean abiotic conditions, but also through increases in the frequency and severity of extreme events. Changes in mean aridity associated with climate change can generate ecotype by environment mismatch (i.e., climatic displacement). At the same time, variability around these shifting means is predicted to increase, resulting in more extreme droughts. We characterized the effects of two axes of climate change–climatic displacement and drought–on the shrub Artemisia californica and its arthropods. We established common gardens of plants sourced along an aridity gradient (3.5-fold variation in MAP) in an arid region of the species distribution, thus generating a gradient of climatic displacement (sustained increase in aridity) as predicted with climate change. We surveyed plants and arthropods over eight years where precipitation varied 6-fold, including both extreme drought and relatively mesic conditions. These two axes of climate change interacted to influence plant performance, such that climatically-displaced populations grew slowly regardless of drought and suffered substantial mortality during drought years. Conversely, local populations grew quickly, increased growth during wet years, and had low mortality regardless of drought. Effects on plant annual arthropod yield were negative and additive, with drought effects exceeding that of climatic displacement by 24%. However, for plant lifetime arthropod yield—incorporating effects on both plant growth and survival—climatic displacement exacerbated the negative effects of drought. Collectively these results demonstrate how climatic displacement (through increasing aridity stress) strengthens the negative effects of drought on plants and, indirectly, on arthropods, suggesting the possibility of climate-mediated trophic collapse. --

Methods From Croy et al (2021):

Artemisia californica (Less. Asteraceae) is a dominant shrub of California’s biodiverse and threatened coastal sage scrub ecosystem (Myers et al. 2000) and supports a species-rich arthropod community (Pratt et al. 2017). The species can live up to 25 years (Sawyer et al. 2009) and relies on wind for pollination and seed-dispersal. This shrub spans a 1,000 km distribution that encompasses a five-fold precipitation gradient from Northern Baja, Mexico (average annual precipitation: 20 cm) to Mendocino County, California (average annual precipitation: 103 cm). Studies have documented genetically-based trait variation across populations of A. californica that is suggestive of locally adapted ecotypes (Pratt and Mooney 2013). These ecotypic differences in turn influence the abundance and community composition of arthropods (Pratt et al. 2017) that are both a key component of biodiversity and support several endemic and endangered vertebrates that drive regional conservation efforts (Bowler 2000). Climate projections for the region include both northward shifts in aridity and an increased frequency and severity of droughts (Diffenbaugh et al. 2015, Wang et al. 2017, Swain et al. 2018; but see Wang et al. (2017) on simultaneous projections of increased deluge), and there is evidence this change is already underway (Pratt and Mooney 2013, MacDonald et al. 2016). This current study is based upon populations of A. californica distributed over 700 km in southern and north-central California (32.8-37.8° latitude; 26.6-91.6 cm precipitation) that together represent 67% of its range and include 80% of the precipitation gradient defining its overall distribution.

Common garden design

This study is based upon the analysis of data from two common gardens initiated in separate years (2009 and 2011) and containing a total of 21 A. californica populations (Appendix S1: Table S1, Figure 1). The site for both gardens is in Newport Beach, CA (33°39’N) and within the Upper Newport Bay Ecological Preserve. Wild A. californica grows within 10 m of the garden perimeter. The site has a mean annual precipitation and temperature (from 1964-2014) of 29.9 cm and 17.6°C, respectively (Appendix S1: Table S1, Fig. 1).

Studying plants sourced from many environments within a common garden serves as a tool for documenting the consequences of environmental displacement, an approach commonly used in forestry provenance studies (O’Brien et al., 2007). Although displacement effects can be attributed to a variety of factors (e.g., climate, soil properties, biotic communities, etc.), we interpret displacement primarily through the lens of variation in aridity for several reasons. First, the coastal sites from which we sample A. californica vary dramatically and clinally with respect to aridity (Table A1). Second, a previous study of these populations demonstrates clinal ecotypic variation in many leaf water relations traits (Pratt and Mooney 2013, Pratt et al. 2014), consistent with local adaptation to an aridity gradient. Third, genetically-based clines in leaf functional traits parallel patterns of arthropod densities along the coast (Pratt et al. 2017), suggesting a bottom-up effect of aridity on plant-quality and associated arthropod densities. We nonetheless recognize that other factors may vary latitudinally and influence plant and arthropod performance, and we discuss the implications accordingly.

The details regarding common garden construction can be found in Appendix S2, but the core design is briefly described here. For the common garden established in 2009 (hereafter the “2009 garden”), cuttings from five A. californica populations were collected along a coastal gradient in spring 2008 and grown within a greenhouse. In December 2009, the common garden was planted into three blocks each containing a pair of plots, one irrigated and the other unirrigated (Pratt and Mooney 2013, Pratt et al. 2014, 2017). While Pratt and Mooney (2013) included a precipitation manipulation that forced plants outside the precipitation that they naturally experienced in Southern California, this study focuses on the unirrigated plots experiencing an ambient Southern California climate. The plants from each source population (sample sizes ranging from 7 to 21 per population) were evenly distributed among plots and randomized within each plot. To minimize non-genetic maternal effects associated with plants cloned from cuttings (Roach and Wulff 1987), rooted cuttings were grown in the greenhouse and common garden for a total of 24 months before collecting data.

The common garden established in 2011 (hereafter the “2011 garden”) is immediately adjacent to the 2009 garden. In December 2010, we collected seed from 10 A. californica plants in each of 21 source populations, including the five populations sampled for the 2009 garden, and germinated the seed in early February 2010 in a greenhouse. In February 2011, approximately ten individuals per population (N = 210 plants total) were transplanted into a common garden and completely randomized within a 14 by 15 m grid. Plants within each garden were lightly irrigated during their first summer following transplant to increase survival.

Climate data

We extracted and averaged 50 years (1964-2013) of monthly precipitation and temperature estimates for each population source site and the common garden from the PRISM database (PRISM Climate Group 2004; Appendix S1: Table S1). We quantify displacement specifically with respect to precipitation as a surrogate for aridity broadly because precipitation is highly correlated with both temperature (r = -0.71) and an aridity metric that incorporates temperature (e.g. Standardized Precipitation-Evapotranspiration Index [SPEI]; Thornthwaite, 1948; R2=0.99). This also enabled us to compare spatial and temporal variation in aridity through an easily interpretable common currency of precipitation. Also, although MAP includes both wet and dry season precipitation, which may have different impacts (Michalet et al. 2021), we find that variation in dry season precipitation along the coast is negligible (Appendix S1: Fig. S1). In parallel, we gathered precipitation data located < 2 km away from our common garden for 2009-2018 from a local weather station (33.67°, -117.89°) maintained by Orange County Watersheds (Appendix S1: Table S2). Because A. californica completes most of its growth during winter and spring rains (DeSimone and Zedler 2001) and we sampled arthropods in May at peak plant biomass (see below), we computed annual precipitation from October 1 to April 30 (i.e., a hydrologic year). Precipitation between May 1 and October 1 is minimal, constituting only 5% of mean annual precipitation.

Plant performance - aboveground biomass and survival

To assess the effects of climate change on aspects of plant performance relevant to arthropods, we measured plant canopy size and survival from 2010-2018 at the conclusion of each growing season (mid-May). To estimate aboveground dry biomass, we collected reference branches from an A. californica shrub outside of our garden plots and visually estimated the total number of such branches needed to reconstruct our experimental shrubs separately for two reference branches. These reference branches were then dried and weighed to estimate shrub dry biomass. Data from 2010 and 2011 in the 2009 garden were based on estimations of canopy volume (Pratt & Mooney, 2013), and we subsequently converted these volume estimates to dry biomass based upon a regression formula (F = 2063.9; P < 0.001; R2= 0.82; n = 455; biomass = 7.4*e-4 + 0.16*e-4*volume). At this time, we also noted plant mortality, assuming that plants first assessed as dead in May of a given year had died during the previous summer and that this was driven by precipitation in the hydrologic year preceding that summer mortality.

Arthropod abundance and composition

Each May from 2010 to 2017 we sampled

We planted 30 seedlings of 13 shade-tolerant tropical montane cloud forest tree species in eight forest sites (3120 seedlings in total) along an elevation gradient (1250 to 2429 m a.s.l.) in central-eastern Mexico. We recorded sapling survival and relative growth rate in height (RGRh) and diameter (RGRd) after eight years. We tested the survival and growth response for all species together to the effect of Climatic Transfer Distance (CTD; the difference between the historical climate at the seed source and the current climate at translocation sites) in terms of mean annual temperature (CTD_MAT), maximum and minimum annual temperatures (CTD_Tmax and CTD_Tmin), mean annual precipitation (CTD_MAP), and climate moisture deficit (CTD_CMD). We also tested the sapling response to the climatic variables at the translocation site. We found evidence to support the decline, although slight, in tree sapling survival and growth across tree species with increasing CTD, thus supporting the hypothesis of a reduction in performance with increasing distance between the climate to which tree species are adapted (historic climate of seed origin) and that at the new translocation sites. Our results support a higher mortality risk caused by increasing CTD in MAP and MAT and a decline in growth caused by the increasing CTD in MAT, Tmax, MAP, and CMD. Although high variation occurred among species, this general pattern still emerged and was consistent for all the climatic variables. Methods A total of eight forest sites were selected to establish the plantings in field common gardens along an elevation gradient (1250 to 2429 m a.s.l.) in Veracruz, Mexico. In each site, a 50 × 55 m plot was delimited, fenced, and 30 seedlings of each of 13 tropical montane cloud forest tree species were transplanted in May-June 2015. Species were distributed at random within each plot and the individuals were planted ~2.6 m apart. A ~1 m radius around each seedling was weeded at planting time, and again after three, six and 12 months. Survival, height, and diameter at the base of all plants were recorded immediately after planting and after 8 years. The relative growth rates in height and diameter (RGRh and RGRd, respectively) were calculated following Hunt (1982). To test the climate sensitivity of the young tree stages, we selected the following five climate variables: mean annual temperature (MAT), mean annual precipitation (MAP), mean minimum temperature in the coldest month (Tmin), mean maximum temperature in the warmest month (Tmax), and Hargreave’s Climate Moisture Deficit (CMD). The climate data for each seed source (average 1961-1990) and translocation site (average 2015-2022) were downloaded from the ClimateNA data portal (https://climatena.ca/), based on Wang et al. (2016). For each georeferenced location of a tree species´ seed source (seed trees), we extracted the climatic variables for the reference period 1961 – 1990, assuming that this represents the climate to which the seed trees were adapted. We refer to this 1961 –1990 period as the “historic climate”. For the field translocation sites, climate data were obtained for the period in which the experimental transplants were actually grown (2015–2022), and this is referred to as the “contemporary climate”. To measure the impact of the difference between the historic and contemporary climates at each planting site, we estimated the Climatic Transfer Distance (CTD), sensu Leites et al. (2012) as: CTD = (contemporary climate at the test site) – (historic climate at the seed source). To evaluate the power of CTD as predictor of tree survival (binomial response variable), a Generalized Linear Mixed Model (GLMM; binomial family and logit link function) was used. CTD was included as a fixed-effects term and tree species as a random-effects term. In each full model, we also included the quadratic term for CTD considering that the optimum (highest survival) was expected to occur at the CTD with the value of zero (or closest to zero), and a decline would occur in either direction from the zero, describing a downward facing parabola. We included the climatic variables at the translocation site that were least correlated with the CTD variable (R < 0.7) to fit the complete models. To fit the GLMM, we ran the function glmer with the package lme4 in R Statistical Environment (version 4.2.2; (2022-10-31)(R Core Team, 2022). To evaluate the relative growth rate in height (RGRh) and diameter (RGRd) as a function of CTD, a Linear Mixed Model (LMM) with a normal error distribution was used. We also included the quadratic term for the CTD. The full models also included the climatic variable at the translocation site that was least correlated with the CTD variable. The function lmer of the package nlme was used to fit the LMM (Cayuela Delgado & De La Cruz, 2022). References Cayuela Delgado, L., & De La Cruz, R. O. T. (2022). Análisis de datos ecológicos en R. México: Ediciones Mundi-Prensa. Hunt, R. (1982). Plant growth curves. The functional approach to plant growth analysis. London, UK: Edward Arnold Ltd. Leites, L. P., Rehfeldt, G. E., Robinson, A. P., Crookston, N. L., & Jaquish, B. (2012). Possibilities and limitations of using historic provenance tests to infer forest species growth responses to climate change. Natural Resource Modeling, 25(3), 409-433. doi:10.1111/j.1939-7445.2012.00129.x R Core Team, R. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.URL https://www.R-project.org/. Wang, T. L., Hamann, A., Spittlehouse, D., & Carroll, C. (2016). Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. Plos One, 11(6), 17. doi:10.1371/journal.pone.0156720

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a satellite-based precipitation dataset for hydrological and climate studies, spanning from 1983 to present. It is the longest satellite-based precipitation record available, with daily data at 0.25° resolution for the 60°S–60°N latitude band.PERSIANN rain rate estimates are generated at 0.25° resolution and calibrated to a monthly merged in-situ and satellite product from the Global Precipitation Climatology Project (GPCP). The model uses Gridded Satellite (GridSat-B1) infrared data at 3-hourly time steps, with the raw output (PERSIANN-B1) bias-corrected and accumulated to produce the daily PERSIANN-CDR.The maps show 31 years (1984–2014) of annual and seasonal median and interquartile range (IQR) data. The median represents the 50th percentile of precipitation, and the IQR reflects the range between the 75th and 25th percentiles, showing data variability. Median and IQR are preferred over mean and standard deviation as they are less influenced by extreme values and better represent non-normally distributed data, such as precipitation, which is skewed and zero-limited.Data and Metadata: NCEIThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.

Information is given on elevational range, vegetation type according to Leopold [29], mean annual temperature (MAT), mean annual precipitation (MAP), days of rain (DR), and days below 0°C according to National Meteorological Service of Mexico (data from 1951–2010) [30].

This case follows the evolution of a low pressure system through its life cycle as it moved from Arkansas to Illinois, and provides a good example of the effects of model biases in the AVN model. The errors introduced to the AVN model due to inherent biases resulted in an incorrect prediction of strong cyclogenesis over the Gulf of Mexico.

For more information, see: http://data.eol.ucar.edu/codiac/projs?COMET_CASE_015