Climate Change Impacts on Maui Water Quality and Lahaina Fire PDF

Maui Water Quality and Lahaina fire Dec 2, 2024  Eutrophication – nutrient pollution Agriculture Domestic waste Stream or Groundwater wastewater Dominated by algae Dominated by corals Reduced coral recruitment Reduced diversity Ecosyst...

Maui Water Quality and Lahaina fire Dec 2, 2024  Eutrophication – nutrient pollution Agriculture Domestic waste Stream or Groundwater wastewater Dominated by algae Dominated by corals Reduced coral recruitment Reduced diversity Ecosystem shifts from corals to algae Fabricius 2011 Coastal Acidification Eutrophication, upwelling and submarine groundwater discharge Sedimentation Runoff, coastal erosion and resuspension Reduced larval recruitment Low diversity Low growth rates Decreased productivity Agriculture Groundwater NO3- increases, particularly near sugarcane 𝛿15N remains constant, indicates fertilizers Bishop et al. 2017 On Maui, injection wells are primarily used for the disposal of treated wastewater from municipal wastewater treatment facilities. These wells inject treated effluent deep into the ground, often into porous volcanic rock formations. While this method is considered a cost-effective Injection wells solution for wastewater disposal, it has raised significant environmental and cultural concerns. Primary Uses of Injection Wells on Maui: Wastewater Disposal: Injection wells are a key part of Maui’s wastewater management system, used by facilities like the Lahaina Wastewater Reclamation Facility. Treated effluent that is not reused (e.g., for irrigation or agriculture) is injected into these wells to dispose of it underground. Effluent Management: Some of the treated wastewater injected is partially filtered by the volcanic rock it passes through. However, a portion of the effluent can travel through subsurface pathways and enter coastal waters, particularly coral reefs. Island-wide survey for 𝛿15N of intertidal algal High 𝛿15N is consistent denitrification, common in wastewater treatment Dailer et al. 2010 Kahekili, West Maui Lahaina Wastewater Reclamation Facility (LWRF) Receives 15-17 million L d-1 (June 2019) Injects 13 million L d-1 USGS  Kahekili, West Maui In Kahekili, West Maui, injection wells are a significant environmental issue, particularly in the context of their impacts on nearshore ecosystems and coral reefs. The Lahaina Wastewater Reclamation Facility is located in this area and relies heavily on injection wells for disposing of treated wastewater. This practice has drawn attention for its role in affecting the nearby Kahekili Herbivore Fisheries Management Area (KHFMA), a protected marine area established to help preserve and restore coral reef ecosystems. Submarine groundwater discharge Injection Wells and Their Impact on Kahekili: Wastewater Disposal: (SGD) The Lahaina facility injects millions of gallons of treated wastewater into deep wells daily. This treated water includes nutrients such as nitrogen and phosphorus, which can travel through subsurface pathways and emerge in coastal waters. Nutrient Pollution and Coral Reefs: Studies using dye tracers have confirmed that effluent from these injection wells Nutrients (120 µM NO3-) flows through underground lava tubes and reaches the ocean at Kahekili Beach Park. The excess nutrients promote the growth of algae, particularly invasive species, which can outcompete and smother coral reefs. This disrupts the delicate balance of reef ecosystems. Cultural and Environmental Significance: For Native Hawaiians, the health of nearshore waters and coral reefs is deeply Acidification (Ω> F * Key Idea: Population dynamics depend on interactions with other species, such as predators, prey, or competitors. * For example, predator populations might influence mortality M(B), or availability of prey could influence recruitmentR(B). * Implication: Growth, recruitment, and/or natural mortality G(B), R(B), M(B) again 2. Inter-specific – dependence on other species outweigh fishing mortality, but the drivers are external ecological relationships rather than the stock’s density. 3. Fishing (Fishing mortality dominates): * Key Idea: Fishing is the dominant factor affecting biomass. * If fishing mortality (F) is much larger than growth, recruitment, and natural G(B), R(B), and/or M(B) >> F mortality (G, R, M), the stock declines primarily because of overfishing. * Implication: This situation requires stricter fisheries management, such as setting quotas, seasonal closures, or gear restrictions. 4. Climate (Environmental effects): * Key Idea: Environmental conditions, such as temperature, currents, and oxygen 3. Fishing - fishing mortality dominates levels, affect growth (G) and recruitment (R). * For example, warmer waters could reduce recruitment success (R(E) or slow growth (G(E). * Natural mortality (M(E) is less likely to be significantly influenced by environmental factors, though it can happen (e.g., hypoxia causing die-offs). F >> G, R, M * Implication: Biomass variability is driven by climate factors, requiring management strategies that consider environmental variability (e.g., adjusting harvest rates during unfavorable conditions). 5. Complexity (Combinations of 1–4): * Key Idea: Biomass variability often results from a combination of density- 4. Climate - environmental effects dependent, ecological, fishing, and environmental factors. * Implication: Fisheries management must address multiple variables simultaneously, making it more complex. Adaptive management and ecosystem- based approaches are often necessary. Relevance to Fisheries Management G(E), R(E) and less likely M(E) This slide emphasizes the need to identify the dominant factors affecting fish stock biomass to develop appropriate management strategies. For example: * If fishing mortality dominates (F >> G, R, M), reducing fishing pressure is essential. 5. Complexity - combinations of 1 to 4 * If environmental or ecological factors are more influential, fisheries management must adapt to those external drivers. * Understanding the interplay of these variables allows for better predictions of stock dynamics and helps maintain sustainable fisheries. Key Definitions from Jennings, Kaiser, & Reynolds 2001 Catch per unit effort (CPUE): catch/some unit of effort Examples include # of hooks, # of tows, hours Biomass=CPUE/q q=catchability coefficient (more on this later) Maximum sustainable yield: the largest catches that can be taken over the long-term without causing the population to collapse Maximum Sustainable Yield Pew Charitable Trust Pauly & Froese 2020 Kobe Plot Kobe Plot This image is a Kobe plot, a tool in fisheries science for visualizing the status of a fish stock relative to its management goals, particularly in terms of biomass and fishing mortality. Axes: X-Axis (SB/SB_MSY): Represents the spawning biomass (SB) of the fish stock as a proportion of the biomass at Maximum Sustainable Yield (SB_MSY). < 1: Stock biomass is below the level needed to achieve MSY (overfished). = 1: Stock biomass is exactly at MSY. > 1: Stock biomass is above the MSY threshold. Y-Axis (F/F_MSY): Represents fishing mortality (F) as a proportion of the fishing mortality at MSY (F_MSY). < 1: Fishing pressure is below the level required to achieve MSY (sustainable). = 1: Fishing pressure is exactly at MSY. > 1: Fishing pressure exceeds sustainable levels (overfishing). Quadrants/Regions: The plot is divided into four color-coded regions based on the combination of SB/SB_MSY (stock biomass) and F/F_MSY (fishing mortality): Green Zone (Bottom Right): SB/SB_MSY > 1 and F/F_MSY < 1. Indicates the stock is healthy (biomass above MSY threshold) and fishing pressure is sustainable. Goal of fisheries management. Orange Zone (Top Right): SB/SB_MSY > 1 and F/F_MSY > 1. Indicates that biomass is high, but fishing pressure is too intense, which could lead to overfishing if not managed. Yellow Zone (Bottom Left): SB/SB_MSY < 1 and F/F_MSY < 1. Indicates that biomass is below sustainable levels, but fishing pressure is low, allowing the stock to potentially recover. Red Zone (Top Left): SB/SB_MSY < 1 and F/F_MSY > 1. Indicates a critical state: the stock is overfished (low biomass) and experiencing unsustainable fishing pressure. Immediate action is needed to reduce fishing and allow recovery. Data Points and Trends: Colored Points: Each point represents the status of the stock in a particular year or time frame. Colors: Likely represent trends over time or different confidence levels in the data. Lines: Show transitions in stock status over time, providing insight into whether management actions have improved or worsened the stock's condition. Current Status: The most recent data point (often highlighted, e.g., green or yellow) indicates the stock's current position on the plot. Key Takeaways from This Plot: Healthy Stocks: Points in the green zone indicate sustainable fishing and healthy biomass levels. Overfishing Concerns: Points in the orange and red zones highlight overfishing issues. Stock Recovery: Movement from the yellow or red zones toward the green zone suggests effective management and stock recovery. Day et al., 2023 Risk of Overfishing: If points move upward or leftward (into red or orange zones), it signals increasing pressure on the stock. Application in Fisheries Management: Assessment: Helps managers evaluate whether a fishery is sustainable based on current biomass and fishing mortality levels. Adjustments: Guides decisions to reduce fishing effort or implement recovery measures. Monitoring: Tracks the impact of management actions over time. This specific Kobe plot likely shows a time series of a stock moving toward sustainability, but some years indicate overfishing or low biomass. The ultimate goal is to maintain stocks in the green zone.  Steepness Graph This graph illustrates the concept of steepness (h) in fisheries stock-recruitment relationships, which describes how the number of recruits (offspring that survive to join the adult population) changes with the size of the spawning stock. Axes: The X-Axis (S/S₀) represents the proportion of the spawning stock size (S) relative to the unfished spawning stock size (S₀). S/S₀ = 1 means the population is at its original unfished “Steepness” A steeper slope (higher steepness value, like size, while S/S₀ < 1 means the population has been reduced due to fishing or environmental factors. The Y-Axis (R/R₀) represents the proportion of recruitment (R) relative to the recruitment that would occur in an unfished population (R₀). Key Lines and Steepness (h): The lines on the graph correspond to different steepness values (h): 0.6, 0.75, and 0.9. Steepness (h) is a parameter in stock-recruitment models that quantifies how resilient a fish population is to reductions in spawning stock. For h = 0.9, h=0.9) means the recruitment remains relatively strong even when the spawning stock size is significantly reduced. For h = 0.75, recruitment declines more noticeably as the spawning stock is population is more reduced. For h = 0.6, recruitment is highly sensitive to reductions in spawning stock, meaning the population is less resilient to fishing pressure or other impacts. resilient. Regions of the Graph: The green region indicates higher recruitment relative to the unfished stock size. Populations with higher steepness (e.g., h = 0.9) are more likely to sustain higher recruitment even at lower spawning stock levels. The orange region indicates a sharp decline in recruitment when the spawning stock size decreases. Populations with lower steepness (e.g., h = 0.6) are more vulnerable to overfishing because recruitment drops off more steeply as the stock is reduced. Interpretation and Fisheries Management: High steepness (h = 0.9) suggests the stock can still produce recruits even when heavily fished, making it more resilient. Low steepness (h = 0.6) indicates a population that is highly dependent on maintaining a large spawning stock for recruitment, making it less resilient. Stocks with low steepness require more conservative management (e.g., stricter quotas or protection of spawning stock) to prevent recruitment failure. Stocks with high steepness are less vulnerable to overfishing but still need careful monitoring to avoid long-term declines. This graph is likely based on a stock-recruitment model, such as the Beverton-Holt or Ricker model, where steepness plays a critical role in predicting sustainable harvest rates and population recovery. Summary: The graph shows how recruitment (R) changes as the spawning stock (S) decreases, with steepness (h) determining the sensitivity of recruitment to stock size. Understanding steepness is crucial for predicting how fish populations respond to fishing pressure and setting sustainable harvest limits. Spawning Stock: Refers to the adult portion of the fish population that is capable of reproducing. It is often measured as Spawning Stock Biomass (SSB), which is the total weight of mature individuals in the population. The size of the spawning stock is critical because it determines the reproductive output of the population (number of eggs produced). Factors that influence the spawning stock include fishing mortality (removal of mature fish), environmental conditions, and natural mortality. Recruits: Refers to the juvenile fish that survive early life stages (egg, larval, and juvenile stages) and grow to enter the adult population or become large enough to be caught in a fishery. Maunder & Deriso 2014 Recruitment is measured as the number of young fish added to the population at a specific stage or age. The success of recruitment depends on factors like spawning stock size, environmental conditions (e.g., water temperature, food availability), and predation.  “Steepness” Maunder & Deriso 2014 Wiff et al., 2018 Wiff et al., 2018  “Steepness” Maunder & Deriso 2014 Wiff et al., 2018 Wiff et al., 2018 “Steepness” Wiff et al., 2018 Issues with MSY? Recruitment variability Natural mortality variability Growth variability Distribution shifts How does climate variability and change affect all of these? Recruitment variability Climate variability and change affect recruitment variability by altering ocean temperatures, currents, Natural mortality variability and food availability, which influence larval survival. Natural mortality variability increases due to Growth variability stressors like hypoxia, heatwaves, and predator-prey imbalances. Growth variability is impacted by Distribution shifts changing water temperatures and primary productivity, affecting metabolic rates and food resources. Distribution shifts occur as species move toward cooler waters or deeper habitats to stay within their thermal tolerance. Recruitment Day et al., 2023  Recruitment Woodworth-Jefcoats & Wren 2020 Day et al., 2023  Borealization Graph This graph likely describes the relationship between the borealization index (a measure of shifts in species or ecosystem traits toward more boreal, or colder-water, conditions) and the logarithm of catch per unit effort (CPUE) for male individuals of a species. Key Components: X-Axis (Borealization Index, lag 1–3): Represents how conditions in the ecosystem are shifting toward more boreal (colder) characteristics, with values indicating the degree of change. Natural Mortality A lag (1–3) suggests the effects are observed over a time delay of 1 to 3 years. Y-Axis (Log Catch per Unit Effort): Reflects the abundance of male individuals in the population, standardized for fishing effort (CPUE). A decrease in CPUE implies a decline in abundance or catchability of the species. Trend (Red Line): The red line shows the relationship between the borealization index and the CPUE for males. The graph indicates that as the borealization index increases (conditions become more boreal), the CPUE for males decreases significantly, particularly after a threshold value (~1 on the index). Shaded Area: Represents the confidence interval or variability around the red line, showing uncertainty in the relationship. Interpretation: This graph suggests that as borealization increases, the catchability or abundance of male individuals declines. This could be due to changes in species distribution, habitat suitability, or physiological stress linked to shifting climate conditions. It highlights how climate-driven ecosystem changes can negatively impact fisheries productivity. This graph could be included under a section on natural mortality because changes in the borealization index (linked to shifting climate conditions) can indirectly or directly influence natural mortality rates. Here’s how: Habitat Mismatch: As ecosystems shift toward more boreal conditions, species may face habitat loss or a mismatch with their environmental preferences, leading to increased stress and vulnerability, which can raise natural mortality. Physiological Stress: Warmer temperatures or changing ocean conditions (e.g., oxygen depletion, salinity changes) associated with borealization may reduce the survivability of certain individuals, especially if they are less tolerant to such changes, increasing natural mortality. Predation and Competition: Borealization can alter predator-prey dynamics or increase competition with species better adapted to the new conditions, leading to higher natural mortality for less resilient species. Lag Effect: The inclusion of a lag (1–3 years) might indicate that the impacts of borealization on mortality (e.g., due to cumulative stress, physiological limits, or indirect effects like predation) take time to manifest in population-level declines. Litzow et al., 2024  Growth Variability Denechaud et al., 2020 Reist et al., 2006 Moving species with limited fishing range  Distribution Shifts Bell et al., 2021  Distribution Shifts Palacio-Abrantes et al., 2022 Additional Complications Stock structure By-catch Compounding acute effects with climate change Serial depletion of fish stocks refers to the progressive overfishing and depletion of one fish stock after another, often as a result of unsustainable fishing practices. This process typically happens in a sequence, where fishers target a specific species or population until it becomes too scarce or unprofitable to harvest, and then shift their efforts to a different stock or species. Over time, this leads to the depletion of multiple fish populations. Serial depletion Overfishing of a Target Stock: High fishing pressure reduces the population size of the initially targeted stock, often driving it below sustainable levels. Depletion can occur faster than the stock’s ability to recover, especially without proper management or monitoring. Shift to Alternative Stocks: Once the initial stock is no longer economically viable, fishers move to other species or populations in the same region or ecosystem. Cascading Effects: The cycle repeats, impacting multiple stocks over time and often disrupting the broader marine ecosystem. Examples: Overfishing of large predators (e.g., cod or tuna) often leads to a shift toward smaller or less desirable species (e.g., squid or forage fish). Serial depletion is also seen geographically, as fisheries expand into previously unexploited regions (e.g., deeper waters or remote areas). Cardinale et al., 2011 By-Catch Catch that is not intended (not targeted) Varies quite a bit by fishery, with some fisheries having little to no by-catch (e.g., spearfishing, purse seine), others with high rates (e.g. trawl, longline) Often the management lever used to regulate a fishery (e.g., protected species interactions, “choke” species)  Examples from US Fisheries Savoca et al., 2020 (Low By-Catch) (Low By-Catch; Rough proxy for climate change) (High By-Catch; Rough proxy for climate change)  The role of fisheries science in management Fisheries scientists give advice, the basis or regulations but they rarely make policy. Management Management of US federal fisheries is done through: Magnuson-Stevens Act Endangered Species Act Marine Mammal Protection Act Management in Hawaiʻi State: Community Federal (within EEZ) International State Fisheries Reef fishes Managed through Bureau of Land and Natural Resources Division of Aquatic Resources in charge of science and management recommendations Enforcement through DoCARE: Division of Conservation And Resources Enforcement Community Based Subsistence Fisheries Area Hāʻena Miloliʻi Kīpahulu Federal (within EEZ) Deep-7 Protected species interactions with longline fisheries Managed through the Western Pacific Fisheries Management Council Composed of managers, stakeholders, Council staff Science goes through a “Science and Statistical Committee” Arm that is in charge of fulfilling Magnuson-Stevens mandates International Large pelagics such as ʻahi, swordfish, sharks Regulated by Regional Fishery Management Organizations Western and Central Pacific Fisheries Commission (WCPFC) Inter-American Tropical Tuna Commission (IATTC) International Scientific Committee on Tuna and Tuna-like Species in the North Pacific Ocean (ISC) References Bell, J. D., Senina, I., Adams, T., Aumont, O., Calmettes, B., Clark, S.,... & Williams, P. (2021). Pathways to sustaining tuna-dependent Pacific Island economies during climate change. Nature sustainability, 4(10), 900-910. Cardinale, M., Nugroho, D., & Jonson, P. (2011). Serial depletion of fishing grounds in an unregulated, open access fishery. Fisheries Research, 108(1), 106-111. Palacios-Abrantes, J., Frölicher, T. L., Reygondeau, G., Sumaila, U. R., Tagliabue, A., Wabnitz, C. C., & Cheung, W. W. (2022). Timing and magnitude of climate-driven range shifts in transboundary fish stocks challenge their management. Global change biology, 28(7), 2312-2326. Savoca, M. S., Brodie, S., Welch, H., Hoover, A., Benaka, L. R., Bograd, S. J., & Hazen, E. L. (2020). Comprehensive bycatch assessment in US fisheries for prioritizing management. Nature Sustainability, 3(6), 472-480. Climate Change and Fisheries Justin Suca [email protected] Swordfish Habitat Compression  Examples from US Fisheries Savoca et al., 2020 Recruitment Chavez et al., 2003 World Fisheries FAO Stock structure Kovach Lab Impacts of Climate Change: Sea Level OCN-310 November 4, 2024  Kulp and Strauss, Nature Comm. 2019 300 million people living along the world's coasts could be hit by devastating flooding by 2050, about three times more than previously estimated. The figure could double to 630 million people affected by 2100 if little is done to rein in greenhouse gas emissions that continue to rise around the planet. Honolulu 2050 (Forbes) https://www.ssfm.com/hawaii-at-risk-from-coastal-flooding-linked-to- climate-change/ Key concepts Sea level, on a global average, is rising by a few mm per year Sea level change not the same everywhere, in fact some places may be seeing dropping sea level When discussing trends, it’s important to understand time range Need to keep in mind that acceleration of a trend is hard (impossible?) to quantify, and this may cause many of the different estimates Sea Level Usually measured with respect to land, but really should be w.r.t. some mean gravitational “distance” from the Earth’s center à geoid Can change for different reasons over different time and space scales Important to distinguish between ”sea level” and inundation Why does sea level change? Local changes (mostly): Tides Ocean circulation patterns Atmospheric pressure (1 mbar ~ 1 cm) Land changes Etc. Global changes: Density of sea water changes (e.g., thermal expansion) Total ocean mass changes (e.g., ice melt) Ocean basin volume changes (e.g., plate tectonics) How to compute “coefficient of thermal expansion of seawater” is 2.1 x 10-4 ºC-1 Assume average depth of ocean is about 4 km 4 km x 1000m/km x 1ºC x 2.1 x 10-4 ºC-1 = 0.84 m (per square meter) 1ºC rise in temperature leads to 84 cm rise in sea level (NOTE: assumes heating over entire ocean) Sea Level Through Geological Time Density of sea water changes (e.g., thermal expansion) Steric changes Total ocean mass changes (e.g., ice melt) Eustatic changes Ocean basin volume changes (e.g., plate tectonics) Isostatic changes Steric Sea Level Change Global/regional change - Warmer ocean = Higher sea level - Heating up the ocean makes the seawater less dense, and it expands to take up more volume - A few centimeters to meters variation Eustatic Sea Level Change Global change - Less ice on land = Higher sea level - Global warming and loss of ice sheets and land glaciers (decades to centuries) - Glacial cycles (100k years) 10 Isostatic Sea Level Change Local tectonic effect Land uplift à Lower relative sea level Land sinking à Higher relative sea level The IPCC Fourth Assessment Report found that thermal expansion accounted for about one-quarter of the observed sea-level rise for 1961–2003, melting of land ice accounted for about half For the last 10 years of that period (1993–2003), the IPCC estimated that thermal expansion and land ice melt each contributed about half to the total sea-level rise In a more recent estimate, for 1993–2008, the contribution from land ice increased to 68 percent, the contribution from thermal expansion decreased to 35 percent, I. Recent estimates of sea level How monitored/measured? What does the record show?  How measured/monitored In situ: Tide Gauges Tide gauge network University of Hawaii Sea Level Center (UHSLC) How measured/monitored Remote: Satellite Satellite altimeter Satellite-derived Sea Level Satellite altimeters have been used to measure the marine geoid TOPEX/Poseidon measured sea surface from 1992; follow-on mission to present Cover globe from 66ºS to 66ºN every 10 days Most changes however are short term Like the El Nino on the next slide What do these data show? Shorter term variability from satellites (last ~30 years) Tide gauges longer-term, in some cases almost 100 years https://uhslc.soest.hawaii.edu/ http://uhslc.soest.hawaii.edu http://uhslc.soest.hawaii.edu http://uhslc.soest.hawaii.edu https://www.psmsl.org/products/trends/ II. Longer-term Sea Level Changes Ice age progression over the last 100,000 years à changes to total mass of the ocean How measured? Using "sequence stratigraphy” scientists have noted off-shore shifts of shorelines associated with a later recovery. The largest of these sedimentary cycles can in some cases be correlated around the world with great confidence. Also use oxygen isotope analysis from sediments 28 Post-Glacial Rise in SL Several times in Earth history that there have been major ice sheets at high latitudes Most recent was in the Quaternary (1.6 Ma ago) and it may not be over yet Within this period there have been several glaciations The most recent from 120,000 to 20,000 years ago So there may be another one on the way  Phanerozoic Eon Paleozoic Mesozoic Era Era Cenozoic Era Tertiary Period Quaternary Period Sea Level Through Geological Time Sea level today is at a relative "high stand" within the Quaternary glacial cycles because of rapid late-Pleistocene and early-Holocene de- glaciation. The ancient shoreline of the last glacial period is now under approximately 120 meters of water.  Last Glacial Maximum: 20,000 years ago Laurentide Ice Sheet, 3-4km thick All this ice caused a EUSTATIC sea level drop of 125m How do we know this? Aerial view of glaciated Bylot Island, Canada U-shaped valley Glacial Striations lo w i alF G lac 34 Last Glacial Maximum Melt-water Pulses (MWP) MWP are “melt-water pulses”, caused by rapid release of water into the ocean from the collapse of continental ice sheets. MWP1A, occurred between 13,500 and 14,700 years ago and global sea level rose between 16 and 25 meters in about 400–500 years, roughly 40–60 mm/yr Longer term changes (tectonics)  The Quaternary sea level fluctuations of 100 m and more were the result of about 50 x 106 km³ of water being alternately withdrawn from and returned to the oceans There remains about 30 x 106 km³ of ice in the polar icecaps Total melting would lead to a further increase of 60 m in sea level  Summary of spatio-temporal scales of sea level change (D) Plate Tectonics 100 m (C) Melting of ICE MSL (meters) Load from ice sheets deforms crust 10 m Thickness and area of continental crust Thermal state (age) of crust (A) Exchange of water with continents sediment loading (Groundwater, Lakes, etc.) (B) Thermal expansion 1m NOTE: A,B,C à change in volume of water D à change in shape of container 1 cm 1 day 100 1000 100 Ka 10 Ma 100 Ma TIME (years) Other processes complicating the study of mean sea level (ice or sediment loads): Post Glacial Rebound Implications: Future Sea Level At present sea level is rising by 2 mm per year. IPCC AR-5 Sea level has exceeded 5 m above present (very high confidence) when global mean temperature was up to 2°C warmer than pre- industrial (medium confidence). Transition in the late 19th century to the early 20th century from relatively low mean rates of rise over the previous two millennia to higher rates of rise (high confidence).  Ocean thermal expansion and glacier melting have been the dominant contributors to 20th century global mean sea level rise. There is high confidence in projections of thermal expansion and Greenland surface mass balance, and medium confidence in projections of glacier mass loss and Antarctic surface mass balance  The sum of thermal expansion, glacier mass loss, and estimates of land water storage explain 65% of the observed global mean sea level rise for 1901–1990 and 90% for 1971–2010 and 1993–2010 (high confidence).  It is virtually certain that global mean sea level rise will continue beyond 2100, with sea level rise due to thermal expansion to continue for many centuries. The amount of longer term sea level rise depends on future emissions. The available evidence indicates that sustained global warming greater than a certain threshold above pre-industrial would lead to the near-complete loss of the Greenland ice sheet over a millennium or more, causing a global mean sea level rise of about 7 m.  It is very likely that in the 21st century and beyond, sea level change will have a strong regional pattern, with some places experiencing significant deviations of local and regional sea level change from the global mean change. It is very likely that there will be a significant increase in the occurrence of future sea level extremes in some regions by 2100, with a likely increase in the early 21st century. Climate Change Impacts: Forests OCN-310 November 6, 2024  Overview Global forests and forestry Forests and the carbon cycle Climate change and forests: Impacts Mitigation Adaptation  Global forests Forest Other wooded land Other land Water Forests comprise »4 billion ha (30% of land surface, 434 billion m3) 89% natural (36% primary and 53% modified) Source: FAO Global Forest Resource Assessment 2005 Where Forests are Found Where Forests are Found  Global forests: recent changes Change 2000 – 2005 Greatest forest loss in low- income, low-latitude countries Average annual net loss: Brazil – 3.1 million ha Indonesia - 1.9 million ha Average annual net gain: China – 4.0 million ha >0.5% decrease per year >0.5% increase per year Change rate