{ "schema": "auturnal.deep-research-review.v2", "slug": "marine-heatwave-baselines", "title": "A Moving Ocean Baseline: Marine Heatwaves, Attribution and Ecological Exposure", "category": "Planetary change", "dek": "Marine heatwaves are defined relative to a baseline, but organisms experience absolute temperature histories.", "english": "Marine heatwaves are defined relative to a baseline, but organisms experience absolute temperature histories. A fixed climatology and a moving climatology answer different questions under warming. This review derives exceedance probabilities, separates duration from frequency, and connects statistical event definitions with ecological exposure without treating either as a universal biological threshold.", "abstract": "Marine heatwaves are defined relative to a baseline, but organisms experience absolute temperature histories. A fixed climatology and a moving climatology answer different questions under warming. This review derives exceedance probabilities, separates duration from frequency, and connects statistical event definitions with ecological exposure without treating either as a universal biological threshold.", "refs": [ "ef-14-1", "ef-14-2", "ef-14-3" ], "sections": [ [ "An event depends on a reference", "A temperature can be exceptional relative to a historical climate and ordinary relative to a warmer recent climate. Neither statement is inherently contradictory. The reference period defines the question. A fixed baseline tracks departure from an established climate, while a moving baseline emphasizes anomalies relative to contemporary conditions. Comparing event counts without aligning those choices can create apparent disagreement.", [] ], [ "Definitions and changing climates", "Hobday and colleagues proposed a hierarchical marine-heatwave definition using a seasonally varying percentile threshold and minimum duration. Frolicher and colleagues investigated changes in marine heatwaves under warming. Chiswell examined the consequences of fixed versus changing baselines. These contributions show that an event catalogue combines a physical time series with an analytical definition.", [ "ef-14-1", "ef-14-2", "ef-14-3" ] ], [ "A mean shift changes exceedance frequency", "Even if variability remains unchanged, shifting the temperature distribution upward increases exceedances of a fixed threshold. This provides a clear null explanation for more frequent threshold events under warming. It does not imply that variability is unimportant; rather, mean, variance and persistence should be examined separately before attributing a change in the catalogue to a particular mechanism.", [] ], [ "Duration requires temporal dependence", "A percentile alone specifies a marginal exceedance probability, not the probability of a multiday event. Consecutive temperatures are correlated, and seasonal dynamics further shape runs. Multiplying daily probabilities as if days were independent can seriously misrepresent event duration. A useful model must preserve the temporal structure relevant to the minimum-duration rule.", [] ], [ "Moving baselines can hide cumulative exposure", "A moving threshold may classify a sustained warm regime as less anomalous even while organisms experience higher absolute temperatures. This is appropriate for some questions about contemporary anomalies, but insufficient for questions about historical displacement or accumulated thermal stress. Reporting both an anomaly measure and an absolute exposure measure prevents the reference choice from silently changing the ecological interpretation.", [] ], [ "Biological thresholds are not universal percentiles", "Species, life stages and acclimation histories differ in thermal response. A statistical 90th percentile is not automatically a mortality threshold. Exposure can depend on depth, nighttime cooling, oxygen availability and previous stress. A surface temperature index therefore needs biological validation before it is translated into predicted ecological damage.", [] ], [ "Sensor coverage shapes the event record", "Satellite observations, buoys and subsurface measurements sample different depths and have different gaps. Missing cloudy periods or changing spatial resolution can affect apparent extremes. Gap-filling methods should be evaluated for their effect on tails and duration, not just average reconstruction error. A smooth interpolation can preserve a mean while erasing a short intense event.", [] ], [ "A useful event catalogue retains its recipe", "The climatological period, percentile estimator, seasonal window, gap rules and duration criteria should accompany every event record. Ecological analyses should then test which exposure metric predicts an independently measured response. The goal is not to choose one index for every purpose, but to make the question answered by each index explicit and reproducible.", [] ], [ "Two baselines answer two legitimate questions", "A fixed historical threshold asks how unusual present conditions are relative to an earlier climate. A moving threshold asks how unusual they are relative to recent conditions. Neither is universally correct: the choice depends on whether the endpoint concerns historical exposure, adaptation or short-term anomalies. A proposed analysis would publish both with their reference periods and avoid switching definitions after seeing the trend. Ecological response may depend on absolute temperature, cumulative duration and recovery intervals, so a percentile-based event catalog should be paired with those quantities before being interpreted as a map of biological stress.", [] ], [ "Persistence is a parameter, not just an event count", "A warmer mean and stronger temporal dependence can both increase long hot spells. The Gaussian exceedance example isolates the mean; the Markov example isolates persistence. A real attribution analysis should test whether the residual process changes after accounting for the trend and seasonal cycle. Resampling individual days destroys serial dependence and can produce misleading uncertainty for event duration. A proposed bootstrap would preserve appropriately chosen blocks or use a validated time-series model. Reporting the sensitivity to block length or model choice makes clear whether a duration trend is robust or rests on a fragile representation of temporal memory.", [] ], [ "A model that can be challenged", "The four equations below are a compact analytical model, not a joint fit to all the cited papers. Their interpretation is as follows. For a Gaussian illustrative temperature distribution, threshold exceedance depends on mean mu, standard deviation sigma and threshold q. The historical Gaussian 90th percentile is an example threshold; operational marine-heatwave thresholds are seasonally estimated. Thermal dose above a chosen biological threshold Tc has temperature-time units; Tc must be justified for the organism and context. This expression applies only to five specified independent days; it is not a realistic event-frequency formula for autocorrelated ocean temperatures.\nThe observational target is event frequency, duration and organism-relevant thermal exposure. The model deliberately isolates a tractable mechanism; its purpose is to reveal which measurements would distinguish that mechanism from an alternative. Parameter values in the figures are declared illustrative inputs. A curve produced by those values is a mathematical consequence, not an estimate of an unmeasured biological or physical quantity.", [] ], [ "A discriminating validation design", "The primary comparison should test whether an exposure metric predicts independent ecological response. The unit of independent replication is independent ocean region and time block; repeated readings from the same unit improve precision but do not create independent replication. A strong validation would hold out different depths and ecological communities before choosing thresholds or fitting nuisance corrections. In particular, baseline choice and temporal autocorrelation must be measured, constrained or varied in a sensitivity analysis. Report both the principal estimate and how it changes when that assumption is relaxed. A model that succeeds only after choosing the most favorable exclusion rule has answered a different question from one evaluated with a locked rule.", [] ], [ "Worked numerical analysis", "With historical mean zero and standard deviation one, a fixed threshold of 1.28155 has approximately 10% exceedance probability. Raising the mean by one standard deviation while keeping variance fixed raises that probability to about 38.9%. A moving threshold shifted by the same amount retains 10% by construction. Neither calculation determines the number of five-day events; the second figure shows why persistence must be represented separately.\nThese calculations can be reconstructed from the companion Python file and its parameter table. Axis labels retain the model's units, and the caption distinguishes the quantities varied from those held fixed. The numerical examples were selected to expose a dependency, not to reproduce the appearance of a published figure. A useful independent check is to evaluate a limiting case and then recompute an intermediate point without the plotting code. Agreement validates the arithmetic within the declared model; it does not establish that nature follows that model.", [] ], [ "Evidence, transfer and next steps", "The scope of the conclusion is limited by baseline choice and temporal autocorrelation. Transfer to different depths and ecological communities requires evidence beyond fitting the original observations. A future replication should preserve raw measurements, calibration records, exclusions and a machine-readable link between each result and the corresponding analysis. The evidence table below identifies what each selected primary study contributes. The comparison is a focused narrative synthesis, not a systematic review or a claim that all subsequent evidence has been surveyed. The proposed study has not been performed here. Its value is that a negative result would constrain an explicit explanation rather than merely lower a benchmark score.", [] ] ], "equations": [ { "tex": "P(T>q)=1-\\Phi((q-\\mu)/\\sigma)", "explanation": "For a Gaussian illustrative temperature distribution, threshold exceedance depends on mean mu, standard deviation sigma and threshold q." }, { "tex": "q_{0.9}=\\mu_0+1.28155\\sigma_0", "explanation": "The historical Gaussian 90th percentile is an example threshold; operational marine-heatwave thresholds are seasonally estimated." }, { "tex": "D=\\sum_t\\max(0,T_t-T_c)\\Delta t", "explanation": "Thermal dose above a chosen biological threshold Tc has temperature-time units; Tc must be justified for the organism and context." }, { "tex": "P(\\text{five exceedances})=p^5", "explanation": "This expression applies only to five specified independent days; it is not a realistic event-frequency formula for autocorrelated ocean temperatures." } ], "table": { "headers": [ "Evidence or design choice", "What it identifies", "What remains unresolved" ], "rows": [ [ "Source 1", "A hierarchical definition formalized percentile and duration criteria.", "An event definition is not a universal biological damage threshold." ], [ "Source 2", "Model analysis examined marine-heatwave changes under warming.", "Results depend on baseline, forcing and modeled variability." ], [ "Source 3", "Baseline analysis clarified fixed and moving reference effects.", "The preferred baseline depends on the scientific question." ], [ "Primary observable", "event frequency, duration and organism-relevant thermal exposure", "baseline choice and temporal autocorrelation" ], [ "Independent test", "whether an exposure metric predicts independent ecological response", "Requires new measurements" ], [ "Transfer set", "different depths and ecological communities", "Not validated by the constructed numerical example" ] ] }, "protocol": [ "Specify event frequency, duration and organism-relevant thermal exposure before fitting.", "Retain independent identifiers for each independent ocean region and time block.", "Separate different depths and ecological communities from model development.", "Measure or bound baseline choice and temporal autocorrelation.", "Record units, calibration and the observation operator.", "Compare the primary model with a named rival explanation.", "Prespecify the test: whether an exposure metric predicts independent ecological response.", "Archive raw data, exclusions, parameter files and executable analysis." ], "limitations": [ "An event definition is not a universal biological damage threshold.", "Results depend on baseline, forcing and modeled variability.", "The preferred baseline depends on the scientific question.", "Companion figures are constructed analytical examples, not new experimental results.", "Transfer to different depths and ecological communities has not been demonstrated by this review." ], "evidence": [ { "source": "ef-14-1", "result": "A hierarchical definition formalized percentile and duration criteria.", "boundary": "An event definition is not a universal biological damage threshold.", "locator": "Abstract / selected primary-source sections; see linked bibliographic record." }, { "source": "ef-14-2", "result": "Model analysis examined marine-heatwave changes under warming.", "boundary": "Results depend on baseline, forcing and modeled variability.", "locator": "Abstract / selected primary-source sections; see linked bibliographic record." }, { "source": "ef-14-3", "result": "Baseline analysis clarified fixed and moving reference effects.", "boundary": "The preferred baseline depends on the scientific question.", "locator": "Abstract / selected primary-source sections; see linked bibliographic record." } ], "research_questions": [ "Which baseline serves a historical-change versus adaptation question?", "How does subsurface exposure differ from surface event classification?", "Which thermal-dose model predicts response across independent populations?" ], "diagram": [ "Temperature history", "Baseline definition", "Event catalogue", "Ecological exposure" ], "keywords": [ "Planetary change", "marine-heatwave-baselines", "measurement", "identifiability" ], "kind": "ai_assisted_research_guide", "author": "Codex (AI)", "original_language": "en", "created_date": "2026-10-22", "published_date": null, "publication_status": "local_draft_pending_owner_upload", "version": 1, "previous_version": null, "analytic": "marine-heatwave-baselines", "peer_reviewed": false, "journal_publication": false, "disclosure": { "authorship": "Source-based research review and original mathematical analysis written by Codex (AI). Cited experiments belong to the cited research teams.", "figures": "Concept cover created with the built-in ImageGen tool. Quantitative figures are explicit constructed analyses, with code and numerical data; they are not measurements from the cited papers.", "review": "This local draft has no independent peer-review or journal-acceptance record." }, "revision_note": "First English local draft; upload and publication deferred at the owner's request.", "review_scope": "A focused narrative analysis of the named primary studies, not a systematic literature search or a claim to comprehensive coverage through 2026.", "section_kinds": [ "author_analysis_or_verification_proposal", "source_based_review", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal", "author_analysis_or_verification_proposal" ] }